day_27_dense_community_detection_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7423be59",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# for the explode statement; convert string to list\n",
    "from ast import literal_eval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "102626ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for visualization\n",
    "from IPython.display import SVG \n",
    "from sknetwork.visualization import svg_graph \n",
    "from sknetwork.data import Bunch \n",
    "from sknetwork.ranking import PageRank \n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "def draw_graph(G, show_names=False, node_size=1, font_size=10, edge_width=0.5):\n",
    "\n",
    "    adjacency = nx.to_scipy_sparse_array(G, nodelist=None, dtype=None, weight='weight', format='csr')\n",
    "    adjacency = csr_matrix(adjacency) # fix to weird sknetwork-csr issue; comment out to troubleshoot\n",
    "    \n",
    "    names = np.array(list(G.nodes))\n",
    "    \n",
    "    graph = Bunch()\n",
    "    graph.adjacency = adjacency\n",
    "    graph.names = np.array(names)\n",
    "    \n",
    "    pagerank = PageRank()\n",
    "    scores = pagerank.fit_transform(adjacency)\n",
    "\n",
    "    if show_names:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, font_size=font_size, node_size=node_size, names=graph.names, width=700, height=500, scores=scores, edge_width=edge_width)\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, node_size=node_size, width=700, height=500, scores = scores, edge_width=edge_width)\n",
    "    \n",
    "    return SVG(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a12aec",
   "metadata": {},
   "source": [
    "# Create Graph using Bipartite Projection on Arxiv Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "443c3cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>authors</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zhenyu Mao</td>\n",
       "      <td>Multi-role Consensus through LLMs Discussions ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jialong Li</td>\n",
       "      <td>Multi-role Consensus through LLMs Discussions ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Munan Li</td>\n",
       "      <td>Multi-role Consensus through LLMs Discussions ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenji Tei</td>\n",
       "      <td>Multi-role Consensus through LLMs Discussions ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Harrison B. Smith</td>\n",
       "      <td>An Agnostic Biosignature Based on Modeling Pan...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             authors                                              title\n",
       "0         Zhenyu Mao  Multi-role Consensus through LLMs Discussions ...\n",
       "1         Jialong Li  Multi-role Consensus through LLMs Discussions ...\n",
       "2           Munan Li  Multi-role Consensus through LLMs Discussions ...\n",
       "3          Kenji Tei  Multi-role Consensus through LLMs Discussions ...\n",
       "4  Harrison B. Smith  An Agnostic Biosignature Based on Modeling Pan..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = 'data/arxiv_artificial_life.csv'\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "df['authors'] = df['authors'].apply(literal_eval)\n",
    "\n",
    "df = df[['authors', 'title']]\n",
    "df = df.explode('authors')\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# fix the & so that scikit-network stops crashing\n",
    "df['authors'] = df['authors'].str.replace('&', 'and')\n",
    "df['title'] = df['title'].str.replace('&', 'and')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a7997ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(df['title']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e5726918",
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms import bipartite\n",
    "\n",
    "B = nx.from_pandas_edgelist(df, source='authors', target='title')\n",
    "G = bipartite.projected_graph(B, df['authors'])\n",
    "\n",
    "G = nx.k_core(G, 2) # no isolates or two-node groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbee3229",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph with 5520 nodes and 16119 edges\n"
     ]
    }
   ],
   "source": [
    "print(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9952d45",
   "metadata": {},
   "source": [
    "# Community Detection + Community Analysis\n",
    "\n",
    "There's two parts to what I am attempting today.\n",
    "- I need to use community detection to identify communities in the network.\n",
    "- I can then use network science and data science to identify interesting communities\n",
    "\n",
    "By interesting, I mean not stars. I am looking for complex ecosystems. So, I will identify communities, then attempt to detect more complex ecosystems by using triangles and density."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19d07b3a",
   "metadata": {},
   "source": [
    "## Community Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "46f88f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import community as community_louvain\n",
    "\n",
    "partition = community_louvain.best_partition(G, resolution=1, random_state=1337) # 10 was good for this network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b81c14c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "community\n",
       "31     87\n",
       "131    62\n",
       "61     51\n",
       "89     44\n",
       "281    39\n",
       "38     38\n",
       "326    38\n",
       "134    38\n",
       "150    36\n",
       "672    34\n",
       "59     30\n",
       "737    30\n",
       "668    30\n",
       "100    28\n",
       "105    26\n",
       "195    24\n",
       "81     24\n",
       "97     23\n",
       "519    23\n",
       "263    21\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_df = pd.DataFrame().from_dict(partition, orient='index')\n",
    "community_df.reset_index(inplace=True)\n",
    "community_df.columns = ['node', 'community']\n",
    "\n",
    "community_df['community'].value_counts()[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ed70761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community</th>\n",
       "      <th>node_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>281</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   community  node_count\n",
       "0         31          87\n",
       "1        131          62\n",
       "2         61          51\n",
       "3         89          44\n",
       "4        281          39"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_node_counts = community_df['community'].value_counts()\n",
    "\n",
    "community_size_df = pd.DataFrame(community_node_counts).reset_index()\n",
    "community_size_df.columns = ['community', 'node_count']\n",
    "\n",
    "# just to speed up this experiment\n",
    "community_size_df = community_size_df[community_size_df['node_count'] > 10]\n",
    "\n",
    "community_size_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77719ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_size_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65a4daaa",
   "metadata": {},
   "source": [
    "## Capture Community Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "88142c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>community</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zhenyu Mao</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jialong Li</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Munan Li</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kenji Tei</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Harrison B. Smith</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                node  community\n",
       "0         Zhenyu Mao          0\n",
       "1         Jialong Li          0\n",
       "2           Munan Li          0\n",
       "3          Kenji Tei          0\n",
       "4  Harrison B. Smith          2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "024410cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community</th>\n",
       "      <th>node_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   community  node_count\n",
       "0         31          87"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_size_df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e3ebd1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_community_nodes(community_df, community):\n",
    "\n",
    "    return sorted(community_df[community_df['community']==community]['node'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c8edef19",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_community_context(community_size_df, community_df, G):\n",
    "\n",
    "    G = G.copy()\n",
    "\n",
    "    communities = {}\n",
    "\n",
    "    for row in community_size_df.iterrows():\n",
    "\n",
    "        data = row[1]\n",
    "        community = data['community']\n",
    "        node_count = data['node_count']\n",
    "        \n",
    "        nodes = get_community_nodes(community_df, community)\n",
    "        G_sub = G.subgraph(nodes)\n",
    "\n",
    "        # starting with density today; will add on more context later\n",
    "        density = nx.density(G_sub)\n",
    "        \n",
    "        communities[community] = {}\n",
    "\n",
    "        communities[community]['G'] = G_sub\n",
    "        communities[community]['density'] = density\n",
    "        communities[community]['node_count'] = len(G_sub.nodes)\n",
    "        \n",
    "    return communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "82447ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "community_context_dict = get_community_context(community_size_df, community_df, G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fb8dc673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>community</th>\n",
       "      <th>G</th>\n",
       "      <th>density</th>\n",
       "      <th>node_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>(Christoph Adami, Ashok Khanna, Thomas Pierrot...</td>\n",
       "      <td>0.387597</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131</td>\n",
       "      <td>(Sergio Greco, Krysia Broda, Angelika Kimmig, ...</td>\n",
       "      <td>0.123744</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>(Naram Mhaisen, Wenbo Wang, Muhammad Umar Janj...</td>\n",
       "      <td>0.138039</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>89</td>\n",
       "      <td>(Zahra Dasht Bozorgi, Fatemeh Akoochekian, Mar...</td>\n",
       "      <td>0.135307</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>281</td>\n",
       "      <td>(Marcel van Gerven, Baihan Lin, Keerthiram Mur...</td>\n",
       "      <td>0.174089</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   community                                                  G   density  \\\n",
       "0         31  (Christoph Adami, Ashok Khanna, Thomas Pierrot...  0.387597   \n",
       "1        131  (Sergio Greco, Krysia Broda, Angelika Kimmig, ...  0.123744   \n",
       "2         61  (Naram Mhaisen, Wenbo Wang, Muhammad Umar Janj...  0.138039   \n",
       "3         89  (Zahra Dasht Bozorgi, Fatemeh Akoochekian, Mar...  0.135307   \n",
       "4        281  (Marcel van Gerven, Baihan Lin, Keerthiram Mur...  0.174089   \n",
       "\n",
       "  node_count  \n",
       "0         87  \n",
       "1         62  \n",
       "2         51  \n",
       "3         44  \n",
       "4         39  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_context_df = pd.DataFrame(community_context_dict).T.reset_index()\n",
    "community_context_df.columns = ['community', 'G', 'density', 'node_count']\n",
    "\n",
    "community_context_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "386bcb66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "community_context_df['density'].plot.hist(bins=50, figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb83c40",
   "metadata": {},
   "source": [
    "#### Investigate Communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "960ade35",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_author_papers(df, G):\n",
    "    \n",
    "    G = G.copy()\n",
    "    \n",
    "    check_nodes = G.nodes\n",
    "\n",
    "    return sorted(df[df['authors'].isin(check_nodes)]['title'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b5ec685",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_communities(community_context_df, min_density, max_density, max_nodes=None, n_communities=10):\n",
    "    \n",
    "    if max_nodes==None:\n",
    "        community_context_df['node_count'].sum()\n",
    "    \n",
    "    community_context_df = community_context_df.copy()\n",
    "\n",
    "    mask = (community_context_df['density']>min_density) \\\n",
    "        & (community_context_df['density']<max_density)  \\\n",
    "        & (community_context_df['node_count']<max_nodes)\n",
    "    \n",
    "    community_context_df = community_context_df[mask].copy()\n",
    "    community_context_df = community_context_df.sample(n_communities) # grabbing ten to see the ranges\n",
    "\n",
    "    community_context_df.sort_values('density', inplace=True)\n",
    "    communities = community_context_df['community'].values[0:n_communities]\n",
    "    \n",
    "    return communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37cedc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_community(G_check):\n",
    "\n",
    "    print('Density: {}'.format(nx.density(G_check)))\n",
    "    print('Node Count: {}'.format(len(G_check.nodes)))\n",
    "    return draw_graph(G_check, show_names=True, node_size=3, edge_width=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4cd8b228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([131, 737,  61, 281, 672, 100, 195,   2, 879, 418], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_density = 0.1\n",
    "max_density = 0.3\n",
    "max_nodes = 100\n",
    "\n",
    "# .1 to .3 has more papers and more variation\n",
    "# .3 to .6 is very cool\n",
    "# .6 to .8 is cool to describe smaller more densely connected groups\n",
    "\n",
    "# i did several iterations to find titles; sparse communities seem to have more and more interesting papers (opinion)\n",
    "\n",
    "communities = fetch_communities(community_context_df, min_density=min_density, max_density=max_density, max_nodes=max_nodes, n_communities=10)\n",
    "communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b86e905b",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "Density: 0.12374405076679006\n",
      "Node Count: 62\n"
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       "<IPython.core.display.SVG object>"
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     },
     "execution_count": 38,
     "metadata": {},
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   ],
   "source": [
    "G_check = community_context_dict[communities[0]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "17a5d5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['(Re)configuration based on model generation',\n",
       " 'A general framework for scientifically inspired explanations in AI',\n",
       " 'Early Time-Series Classification Algorithms: An Empirical Comparison',\n",
       " 'FO(C): A Knowledge Representation Language of Causality',\n",
       " 'Fast Frequent Querying with Lazy Control Flow Compilation',\n",
       " 'Incremental Learning of Event Definitions with Inductive Logic Programming',\n",
       " 'Measuring Acoustics with Collaborative Multiple Agents',\n",
       " 'Object Placement on Cluttered Surfaces: A Nested Local Search Approach',\n",
       " 'Problife: a Probabilistic Game of Life',\n",
       " 'Proceedings 35th International Conference on Logic Programming (Technical Communications)',\n",
       " 'Proceedings 36th International Conference on Logic Programming (Technical Communications)',\n",
       " 'Proceedings 37th International Conference on Logic Programming (Technical Communications)',\n",
       " 'Proceedings 38th International Conference on Logic Programming',\n",
       " 'Querying Biomedical Ontologies in Natural Language using Answer Set',\n",
       " 'The KB paradigm and its application to interactive configuration']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#Efficient Top-k Ego-Betweenness Search\n",
    "#Fast Algorithms for Intimate-Core Group Search in Weighted Graphs\n",
    "#Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks\n",
    "#Mining Bursting Communities in Temporal Graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "91953657",
   "metadata": {},
   "outputs": [
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      "Node Count: 30\n"
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       "<text text-anchor=\"start\" x=\"76\" y=\"390\" font-size=\"10\">Alan Dorin</text><text text-anchor=\"start\" x=\"204\" y=\"250\" font-size=\"10\">Mark A. Bedau</text><text text-anchor=\"start\" x=\"433\" y=\"464\" font-size=\"10\">Nicholas Guttenberg</text><text text-anchor=\"start\" x=\"567\" y=\"122\" font-size=\"10\">Daniel Polani</text><text text-anchor=\"start\" x=\"26\" y=\"158\" font-size=\"10\">Ricard V. Sole</text><text text-anchor=\"start\" x=\"80\" y=\"116\" font-size=\"10\">Hans-Joachim Ziock</text><text text-anchor=\"start\" x=\"625\" y=\"20\" font-size=\"10\">Andrei D. Robu</text><text text-anchor=\"start\" x=\"726\" y=\"411\" font-size=\"10\">Georgina Montserrat Reséndiz-Benhumea</text><text text-anchor=\"start\" x=\"632\" y=\"520\" font-size=\"10\">Natalya Weber</text><text text-anchor=\"start\" x=\"367\" y=\"254\" font-size=\"10\">Takashi Ikegami</text><text text-anchor=\"start\" x=\"686\" y=\"54\" font-size=\"10\">Christoph Salge</text><text text-anchor=\"start\" x=\"369\" y=\"149\" font-size=\"10\">Yasuhiro Hashimoto</text><text text-anchor=\"start\" x=\"704\" y=\"449\" font-size=\"10\">Ekaterina Sangati</text><text text-anchor=\"start\" x=\"127\" y=\"411\" font-size=\"10\">Kevin Korb</text><text text-anchor=\"start\" x=\"186\" y=\"307\" font-size=\"10\">Tim Taylor</text><text text-anchor=\"start\" x=\"684\" y=\"503\" font-size=\"10\">Werner Koch</text><text text-anchor=\"start\" x=\"159\" y=\"202\" font-size=\"10\">Steen Rasmussen</text><text text-anchor=\"start\" x=\"480\" y=\"176\" font-size=\"10\">Martin Biehl</text><text text-anchor=\"start\" x=\"243\" y=\"234\" font-size=\"10\">Alastair Channon</text><text text-anchor=\"start\" x=\"495\" y=\"403\" font-size=\"10\">Matthieu Laneuville</text><text text-anchor=\"start\" x=\"495\" y=\"452\" font-size=\"10\">Nathanael Aubert-Kato</text><text text-anchor=\"start\" x=\"576\" y=\"406\" font-size=\"10\">Tom Froese</text><text text-anchor=\"start\" x=\"420\" y=\"408\" font-size=\"10\">Nathaniel Virgo</text><text text-anchor=\"start\" x=\"434\" y=\"392\" font-size=\"10\">Melissa Ilardo</text><text text-anchor=\"start\" x=\"238\" y=\"270\" font-size=\"10\">Norman Packard</text><text text-anchor=\"start\" x=\"711\" y=\"102\" font-size=\"10\">Stavros Anagnou</text><text text-anchor=\"start\" x=\"635\" y=\"56\" font-size=\"10\">Chrystopher L. Nehaniv</text><text text-anchor=\"start\" x=\"395\" y=\"515\" font-size=\"10\">Alexandra Penn</text><text text-anchor=\"start\" x=\"32\" y=\"119\" font-size=\"10\">Harold Fellermann</text><text text-anchor=\"start\" x=\"320\" y=\"163\" font-size=\"10\">Mizuki Oka</text></svg>"
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     "execution_count": 40,
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   "source": [
    "G_check = community_context_dict[communities[1]]['G']\n",
    "draw_community(G_check)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 41,
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    {
     "data": {
      "text/plain": [
       "['Action and perception for spatiotemporal patterns',\n",
       " 'An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue',\n",
       " 'An informational study of the evolution of codes and of emerging concepts in populations of agents',\n",
       " 'Artificial Life and the Web: WebAL Comes of Age',\n",
       " 'Changing the Environment based on Intrinsic Motivation',\n",
       " 'Digital Genesis: Computers, Evolution and Artificial Life',\n",
       " 'Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems',\n",
       " 'Informational parasites in code evolution',\n",
       " 'Levels of Coupling in Dyadic Interaction: An Analysis of Neural and Behavioral Complexity',\n",
       " 'Life cycle of a minimal protocell - a dissipative particle dynamics (DPD) study',\n",
       " 'Measuring Time with Minimal Clocks',\n",
       " 'Motility at the origin of life: Its characterization and a model',\n",
       " 'On the potential for open-endedness in neural networks',\n",
       " 'Open-ended Evolution and a Mechanism of Novelties in Web Services',\n",
       " 'Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines',\n",
       " 'Requirements for Open-Ended Evolution in Natural and Artificial Systems',\n",
       " 'Scaling up the self-optimization model by means of on-the-fly computation of weights',\n",
       " 'Self-Reproduction and Evolution in Cellular Automata: 25 Years after Evoloops',\n",
       " 'Self-organization on social media: endo-exo bursts and baseline fluctuations',\n",
       " 'The Effect of Noise on the Emergence of Continuous Norms and its Evolutionary Dynamics',\n",
       " 'The Importance of Open-Endedness (for the Sake of Open-Endedness)',\n",
       " 'Time as it could Be measured in Artificial Living Systems',\n",
       " 'Towards information based spatiotemporal patterns as a foundation for agent representation in dynamical systems',\n",
       " 'Transferable measurements of Heredity in models of the Origins of Life',\n",
       " 'WebAL-1: Workshop on Artificial Life and the Web 2014 Proceedings']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#A Deep Learning Approach for Multimodal Deception Detection\n",
    "#GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts\n",
    "#RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media\n",
    "#Capturing Topology in Graph Pattern Matching\n",
    "#Graph Pattern Matching for Dynamic Team Formation\n",
    "#SceneRec: Scene-Based Graph Neural Networks for Recommender Systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "359b8f0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.1380392156862745\n",
      "Node Count: 51\n"
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     "execution_count": 42,
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   ],
   "source": [
    "G_check = community_context_dict[communities[2]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f677b9c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A Survey of Toxic Comment Classification Methods',\n",
       " 'Agent-based computing from multi-agent systems to agent-based Models: a visual survey',\n",
       " 'Applications of Deep Learning and Reinforcement Learning to Biological Data',\n",
       " 'Beyond Reality: The Pivotal Role of Generative AI in the Metaverse',\n",
       " 'Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning',\n",
       " 'Deep Learning in Mining Biological Data',\n",
       " 'Federated Learning for 6G Communications: Challenges, Methods, and Future Directions',\n",
       " 'Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey',\n",
       " 'Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior',\n",
       " 'Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence',\n",
       " 'Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach',\n",
       " 'Towards Green Metaverse Networking Technologies, Advancements and Future Directions',\n",
       " 'TranDRL: A Transformer-Driven Deep Reinforcement Learning Enabled Prescriptive Maintenance Framework']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#A Survey of Neural Trojan Attacks and Defenses in Deep Learning\n",
    "#Physical world assistive signals for deep neural network classifiers -- neither defense nor attack\n",
    "#PRAT: PRofiling Adversarial aTtacks\n",
    "#Four hot DOGs in the microwave\n",
    "#Outlyingness: why do outliers lie out?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9ca31eed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.17408906882591094\n",
      "Node Count: 39\n"
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     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G_check = community_context_dict[communities[3]]['G']\n",
    "draw_community(G_check)"
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  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7a9b992e",
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "['A Survey on Compositional Generalization in Applications',\n",
       " 'Building Ethically Bounded AI',\n",
       " 'Comprehensive Multi-Agent Epistemic Planning',\n",
       " 'Context Attentive Bandits: Contextual Bandit with Restricted Context',\n",
       " 'Contextual Bandit with Missing Rewards',\n",
       " \"Dealing with incomplete agents' preferences and an uncertain agenda in group decision making via sequential majority voting\",\n",
       " 'Double-Linear Thompson Sampling for Context-Attentive Bandits',\n",
       " \"Etat de l'art sur l'application des bandits multi-bras\",\n",
       " 'Fast and Slow Planning',\n",
       " 'Incorporating Behavioral Constraints in Online AI Systems',\n",
       " 'Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach',\n",
       " 'NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)',\n",
       " 'Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World',\n",
       " 'Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward',\n",
       " 'Reasoning about soft constraints and conditional preferences: complexity results and approximation techniques',\n",
       " 'Stable marriage problems with quantitative preferences',\n",
       " 'Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#Natural Backdoor Attack on Text Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2b37f91e",
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
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      "Node Count: 34\n"
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     "execution_count": 46,
     "metadata": {},
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   ],
   "source": [
    "G_check = community_context_dict[communities[4]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9c4528ab",
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "['AutoSTL: Automated Spatio-Temporal Multi-Task Learning',\n",
       " 'Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study',\n",
       " 'DialogSum: A Real-Life Scenario Dialogue Summarization Dataset',\n",
       " 'FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop',\n",
       " 'Federated Forest',\n",
       " 'Federated Transfer Reinforcement Learning for Autonomous Driving',\n",
       " 'Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text',\n",
       " 'Universal controlled-phase gate with cat-state qubits in circuit QED']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#Entropy and the Predictability of Online Life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4b8c31db",
   "metadata": {},
   "outputs": [
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   "source": [
    "G_check = community_context_dict[communities[5]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "202cd434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs',\n",
       " 'An artifcial life approach to studying niche differentiation in soundscape ecology',\n",
       " 'Co-generation of game levels and game-playing agents',\n",
       " 'Growing 3D Artefacts and Functional Machines with Neural Cellular Automata',\n",
       " 'Increasing Behavioral Complexity for Evolved Virtual Creatures with the ESP Method',\n",
       " 'Quality Diversity in the Amorphous Fortress (QD-AF): Evolving for Complexity in 0-Player Games',\n",
       " 'Quality Evolvability ES: Evolving Individuals With a Distribution of Well Performing and Diverse Offspring',\n",
       " 'Say \"Sul Sul!\" to SimSim, A Sims-Inspired Platform for Sandbox Game AI',\n",
       " 'The Case for a Mixed-Initiative Collaborative Neuroevolution Approach',\n",
       " 'Transfer Dynamics in Emergent Evolutionary Curricula']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#XENON1T Anomaly and its Implication for Decaying Warm Dark Matter\n",
    "#Wandering in Color-Space -- why the life of pentaquark is so long ? --\n",
    "#What can we learn from universal Turing machines?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1dc8c070",
   "metadata": {},
   "outputs": [
    {
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      "Node Count: 24\n"
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    "G_check = community_context_dict[communities[6]]['G']\n",
    "draw_community(G_check)"
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   "execution_count": 51,
   "id": "97e20914",
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      "text/plain": [
       "['Hypernetworks build Implicit Neural Representations of Sounds',\n",
       " 'I Know How You Feel: Emotion Recognition with Facial Landmarks',\n",
       " 'Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning',\n",
       " 'ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging',\n",
       " 'ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification',\n",
       " 'RegFlow: Probabilistic Flow-based Regression for Future Prediction']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#Causal Explanation Analysis on Social Media\n",
    "#Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learnin\n",
    "#The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions\n",
    "#G-networks and the optimization of supply chains\n",
    "#Lady and the Tramp Nextdoor: Online Manifestations of Economic Inequalities in the Nextdoor Social Network"
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  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d9f6d485",
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   "outputs": [
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    "G_check = community_context_dict[communities[7]]['G']\n",
    "draw_community(G_check)"
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   "cell_type": "code",
   "execution_count": 53,
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       "['An Agnostic Biosignature Based on Modeling Panspermia and Terraformation',\n",
       " 'An active inference implementation of phototaxis',\n",
       " \"DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Ability to Learn Discrete-World Dataset\",\n",
       " 'Evolutionary Generation of Visual Motion Illusions',\n",
       " 'Evolutionary Transitions and Top-Down Causation',\n",
       " 'Exploring the Adaptive Behaviors of Particle Lenia: A Perturbation-Response Analysis for Computational Agency',\n",
       " 'False positives and the challenge of testing the alien hypothesis',\n",
       " 'Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems',\n",
       " \"Life's Chirality From Prebiotic Environments\",\n",
       " 'Perspective: Purposeful Failure in Artificial Life and Artificial Intelligence',\n",
       " 'The Chirality Of Life: From Phase Transitions To Astrobiology',\n",
       " 'The Emergence of Life as a First Order Phase Transition',\n",
       " 'The Futility of Exoplanet Biosignatures',\n",
       " 'The modularity of action and perception revisited using control theory and active inference']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#Real-Time Bidding by Reinforcement Learning in Display Advertising\n",
    "#Ease-of-Teaching and Language Structure from Emergent Communication\n",
    "#The Hanabi Challenge: A New Frontier for AI Research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1712913d",
   "metadata": {},
   "outputs": [
    {
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      "Node Count: 12\n"
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    "G_check = community_context_dict[communities[8]]['G']\n",
    "draw_community(G_check)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e980d956",
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    {
     "data": {
      "text/plain": [
       "['Being Bayesian about Network Structure',\n",
       " 'Convex Point Estimation using Undirected Bayesian Transfer Hierarchies',\n",
       " 'Exact Inference in Networks with Discrete Children of Continuous Parents',\n",
       " 'Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks',\n",
       " 'Gaussian Process Networks',\n",
       " 'Learning Bayesian Network Structure from Massive Datasets: The \"Sparse Candidate\" Algorithm',\n",
       " 'Learning the Dimensionality of Hidden Variables',\n",
       " 'Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing',\n",
       " 'Speedy Model Selection (SMS) for Copula Models',\n",
       " 'The Information Bottleneck EM Algorithm']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#Large Graph Models: A Perspective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0a2d6caa",
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"start\" x=\"426\" y=\"20\" font-size=\"10\">Richard A. Watson</text><text text-anchor=\"start\" x=\"379\" y=\"189\" font-size=\"10\">Simon T. Powers</text><text text-anchor=\"start\" x=\"180\" y=\"520\" font-size=\"10\">Fiona Polack</text><text text-anchor=\"start\" x=\"272\" y=\"22\" font-size=\"10\">Christopher Heys</text><text text-anchor=\"start\" x=\"726\" y=\"180\" font-size=\"10\">Emma Hart</text><text text-anchor=\"start\" x=\"235\" y=\"289\" font-size=\"10\">Eva Reindl</text><text text-anchor=\"start\" x=\"636\" y=\"229\" font-size=\"10\">Jeremy Pitt</text><text text-anchor=\"start\" x=\"199\" y=\"182\" font-size=\"10\">Nathan A. Brooks</text><text text-anchor=\"start\" x=\"159\" y=\"351\" font-size=\"10\">James M. Borg</text><text text-anchor=\"start\" x=\"82\" y=\"293\" font-size=\"10\">Andrew Buskell</text><text text-anchor=\"start\" x=\"714\" y=\"272\" font-size=\"10\">Cedric Perret</text><text text-anchor=\"start\" x=\"26\" y=\"506\" font-size=\"10\">Matt Grove</text><text text-anchor=\"start\" x=\"39\" y=\"238\" font-size=\"10\">Claudio Tennie</text><text text-anchor=\"start\" x=\"571\" y=\"44\" font-size=\"10\">Alexandra S. Penn</text><text text-anchor=\"start\" x=\"144\" y=\"239\" font-size=\"10\">Rohan Kapitany</text></svg>"
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    "G_check = community_context_dict[communities[9]]['G']\n",
    "draw_community(G_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ea542ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A mechanism to promote social behaviour in household load balancing',\n",
       " 'Can justice be fair when it is blind? How social network structures can promote or prevent the evolution of despotism',\n",
       " 'Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems',\n",
       " 'Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research',\n",
       " 'How to measure group selection in real-world populations',\n",
       " 'The efficacy of group selection is increased by coexistence dynamics within groups']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_author_papers(df, G_check)\n",
    "\n",
    "#Does the evolution of complex life depend on the stellar spectral energy distribution?\n",
    "#Funding the Search for Extraterrestrial Intelligence with a Lottery Bond\n",
    "#Why do we find ourselves around a yellow star instead of a red star?\n",
    "#Find the Conversation Killers: a Predictive Study of Thread-ending Posts -- HELL YES"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a3146f",
   "metadata": {},
   "source": [
    "# Get Papers of Interest\n",
    "\n",
    "In a separate text file, I add the papers I am interested in reading. I can then copy and paste them here, and do a quick 'split' to convert it into a useful list, which I can use to get URLs from the original DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ebf963a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "papers = \"\"\"\n",
    "Efficient Top-k Ego-Betweenness Search\n",
    "Fast Algorithms for Intimate-Core Group Search in Weighted Graphs\n",
    "Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks\n",
    "Mining Bursting Communities in Temporal Graphs\n",
    "A Deep Learning Approach for Multimodal Deception Detection\n",
    "GraphTMT: Unsupervised Graph-based Topic Modeling from Video Transcripts\n",
    "RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media\n",
    "Capturing Topology in Graph Pattern Matching\n",
    "Graph Pattern Matching for Dynamic Team Formation\n",
    "SceneRec: Scene-Based Graph Neural Networks for Recommender Systems\n",
    "A Survey of Neural Trojan Attacks and Defenses in Deep Learning\n",
    "Physical world assistive signals for deep neural network classifiers -- neither defense nor attack\n",
    "PRAT: PRofiling Adversarial aTtacks\n",
    "Four hot DOGs in the microwave\n",
    "Outlyingness: why do outliers lie out?\n",
    "Natural Backdoor Attack on Text Data\n",
    "Entropy and the Predictability of Online Life\n",
    "XENON1T Anomaly and its Implication for Decaying Warm Dark Matter\n",
    "Wandering in Color-Space -- why the life of pentaquark is so long ? --\n",
    "What can we learn from universal Turing machines?\n",
    "Causal Explanation Analysis on Social Media\n",
    "Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learnin\n",
    "The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions\n",
    "G-networks and the optimization of supply chains\n",
    "Lady and the Tramp Nextdoor: Online Manifestations of Economic Inequalities in the Nextdoor Social Network\n",
    "Real-Time Bidding by Reinforcement Learning in Display Advertising\n",
    "Ease-of-Teaching and Language Structure from Emergent Communication\n",
    "The Hanabi Challenge: A New Frontier for AI Research\n",
    "Large Graph Models: A Perspective\n",
    "Does the evolution of complex life depend on the stellar spectral energy distribution?\n",
    "Funding the Search for Extraterrestrial Intelligence with a Lottery Bond\n",
    "Why do we find ourselves around a yellow star instead of a red star?\n",
    "Find the Conversation Killers: a Predictive Study of Thread-ending Posts\n",
    "SCB-dataset: A Dataset for Detecting Student Classroom Behavior\n",
    "SCB-Dataset3: A Benchmark for Detecting Student Classroom Behavior\n",
    "Did life originate from low-temperature areas of the Universe?\n",
    "BEAMERS: Brain-Engaged, Active Music-based Emotion Regulation System\n",
    "Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4\n",
    "Local Causal Structure Learning and its Discovery Between Type 2 Diabetes and Bone Mineral Density\n",
    "Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods\n",
    "SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis\n",
    "Secure Artificial Intelligence of Things for Implicit Group Recommendations\n",
    "Thinking Fast and Slow in Large Language Models\n",
    "Cultures in Community Question Answering\n",
    "On the Behaviour of Deviant Communities in Online Social Networks\n",
    "Uncovering Bias in Personal Informatics\n",
    "Computational Metacognition\n",
    "Large Language Models for User Interest Journeys\n",
    "A Survey on Personality-Aware Recommendation Systems\n",
    "MES-P: an Emotional Tonal Speech Dataset in Mandarin Chinese with Distal and Proximal Labels\n",
    "Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements\n",
    "AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020\n",
    "Malware Detection using Artificial Bee Colony Algorithm\n",
    "Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtag\n",
    "The Systemic Impact of Deplatforming on Social Media\n",
    "The Twitter of Babel: Mapping World Languages through Microblogging Platforms\n",
    "Mining the Social Media Data for a Bottom-Up Evaluation of Walkability\n",
    "A Large-scale Friend Suggestion Architecture\n",
    "Towards Cognitive Bots: Architectural Research Challenges\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7eae7f8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Efficient Top-k Ego-Betweenness Search',\n",
       " 'Fast Algorithms for Intimate-Core Group Search in Weighted Graphs',\n",
       " 'Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks',\n",
       " 'Mining Bursting Communities in Temporal Graphs',\n",
       " 'A Deep Learning Approach for Multimodal Deception Detection']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "papers_of_interest = [p for p in papers.split('\\n') if len(p)>0]\n",
    "papers_of_interest[0:5] # preview of first five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7a23cbeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_published</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>summary</th>\n",
       "      <th>url</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-01 12:38:13+00:00</td>\n",
       "      <td>A Deep Learning Approach for Multimodal Decept...</td>\n",
       "      <td>['Gangeshwar Krishnamurthy', 'Navonil Majumder...</td>\n",
       "      <td>Automatic deception detection is an important ...</td>\n",
       "      <td>http://arxiv.org/pdf/1803.00344v1</td>\n",
       "      <td>cs.CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-17 15:58:07+00:00</td>\n",
       "      <td>Does the evolution of complex life depend on t...</td>\n",
       "      <td>['Jacob Haqq-Misra']</td>\n",
       "      <td>This paper presents the proportional evolution...</td>\n",
       "      <td>http://arxiv.org/pdf/1905.07343v1</td>\n",
       "      <td>astro-ph.EP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-02-15 04:26:44+00:00</td>\n",
       "      <td>A Survey of Neural Trojan Attacks and Defenses...</td>\n",
       "      <td>['Jie Wang', 'Ghulam Mubashar Hassan', 'Naveed...</td>\n",
       "      <td>Artificial Intelligence (AI) relies heavily on...</td>\n",
       "      <td>http://arxiv.org/pdf/2202.07183v1</td>\n",
       "      <td>cs.CR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-10 05:07:30+00:00</td>\n",
       "      <td>Thinking Fast and Slow in Large Language Models</td>\n",
       "      <td>['Thilo Hagendorff', 'Sarah Fabi', 'Michal Kos...</td>\n",
       "      <td>Large language models (LLMs) are currently at ...</td>\n",
       "      <td>http://arxiv.org/pdf/2212.05206v2</td>\n",
       "      <td>cs.CL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-03-24 13:24:41+00:00</td>\n",
       "      <td>Machine Psychology: Investigating Emergent Cap...</td>\n",
       "      <td>['Thilo Hagendorff']</td>\n",
       "      <td>Large language models (LLMs) are currently at ...</td>\n",
       "      <td>http://arxiv.org/pdf/2303.13988v4</td>\n",
       "      <td>cs.CL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date_published  \\\n",
       "0  2018-03-01 12:38:13+00:00   \n",
       "1  2019-05-17 15:58:07+00:00   \n",
       "2  2022-02-15 04:26:44+00:00   \n",
       "3  2022-12-10 05:07:30+00:00   \n",
       "4  2023-03-24 13:24:41+00:00   \n",
       "\n",
       "                                               title  \\\n",
       "0  A Deep Learning Approach for Multimodal Decept...   \n",
       "1  Does the evolution of complex life depend on t...   \n",
       "2  A Survey of Neural Trojan Attacks and Defenses...   \n",
       "3    Thinking Fast and Slow in Large Language Models   \n",
       "4  Machine Psychology: Investigating Emergent Cap...   \n",
       "\n",
       "                                             authors  \\\n",
       "0  ['Gangeshwar Krishnamurthy', 'Navonil Majumder...   \n",
       "1                               ['Jacob Haqq-Misra']   \n",
       "2  ['Jie Wang', 'Ghulam Mubashar Hassan', 'Naveed...   \n",
       "3  ['Thilo Hagendorff', 'Sarah Fabi', 'Michal Kos...   \n",
       "4                               ['Thilo Hagendorff']   \n",
       "\n",
       "                                             summary  \\\n",
       "0  Automatic deception detection is an important ...   \n",
       "1  This paper presents the proportional evolution...   \n",
       "2  Artificial Intelligence (AI) relies heavily on...   \n",
       "3  Large language models (LLMs) are currently at ...   \n",
       "4  Large language models (LLMs) are currently at ...   \n",
       "\n",
       "                                 url     category  \n",
       "0  http://arxiv.org/pdf/1803.00344v1        cs.CL  \n",
       "1  http://arxiv.org/pdf/1905.07343v1  astro-ph.EP  \n",
       "2  http://arxiv.org/pdf/2202.07183v1        cs.CR  \n",
       "3  http://arxiv.org/pdf/2212.05206v2        cs.CL  \n",
       "4  http://arxiv.org/pdf/2303.13988v4        cs.CL  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the papers; reload data to get original format\n",
    "\n",
    "data = 'data/arxiv_artificial_life.csv'\n",
    "\n",
    "df = pd.read_csv(data)\n",
    "\n",
    "df = df[df['title'].isin(papers_of_interest)]\n",
    "df.sort_values('date_published', inplace=True)\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1a03cb48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['cs.CL', 'astro-ph.EP', 'cs.CR', 'cs.AI'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories_by_size = df['category'].value_counts().index.values\n",
    "categories_by_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "389ab02e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category: cs.CL\n",
      "\n",
      "Title: A Deep Learning Approach for Multimodal Deception Detection\n",
      "Date Published: 2018-03-01 12:38:13+00:00\n",
      "URL: http://arxiv.org/pdf/1803.00344v1\n",
      "\n",
      "Title: Thinking Fast and Slow in Large Language Models\n",
      "Date Published: 2022-12-10 05:07:30+00:00\n",
      "URL: http://arxiv.org/pdf/2212.05206v2\n",
      "\n",
      "Title: Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods\n",
      "Date Published: 2023-03-24 13:24:41+00:00\n",
      "URL: http://arxiv.org/pdf/2303.13988v4\n",
      "\n",
      "Title: Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models -- and Disappeared in GPT-4\n",
      "Date Published: 2023-06-13 08:43:13+00:00\n",
      "URL: http://arxiv.org/pdf/2306.07622v2\n",
      "\n",
      "Category: astro-ph.EP\n",
      "\n",
      "Title: Does the evolution of complex life depend on the stellar spectral energy distribution?\n",
      "Date Published: 2019-05-17 15:58:07+00:00\n",
      "URL: http://arxiv.org/pdf/1905.07343v1\n",
      "\n",
      "Category: cs.CR\n",
      "\n",
      "Title: A Survey of Neural Trojan Attacks and Defenses in Deep Learning\n",
      "Date Published: 2022-02-15 04:26:44+00:00\n",
      "URL: http://arxiv.org/pdf/2202.07183v1\n",
      "\n",
      "Category: cs.AI\n",
      "\n",
      "Title: Towards Cognitive Bots: Architectural Research Challenges\n",
      "Date Published: 2023-05-26 23:51:49+00:00\n",
      "URL: http://arxiv.org/pdf/2305.17308v1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for category in categories_by_size:\n",
    "    \n",
    "    print('Category: {}'.format(category))\n",
    "    print()\n",
    "\n",
    "    category_df = df[df['category']==category][['title', 'url', 'date_published']].copy()\n",
    "    \n",
    "    for row in category_df.iterrows():\n",
    "        \n",
    "        data = row[1]\n",
    "        title = data['title']\n",
    "        url = data['url']\n",
    "        date_published = data['date_published']\n",
    "        \n",
    "        print('Title: {}\\nDate Published: {}\\nURL: {}'.format(title, date_published, url))\n",
    "        print()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b86262",
   "metadata": {},
   "source": [
    "This last box proved very useful in seeing papers by category and date, to quickly skip papers that looked a bit too old. Use this to skim your papers of interest.  \n",
    "\n",
    "I also noticed that arXiv returns the same paper duplicated by category, so that is something to deduplicated on another day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bb364aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "476b40c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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