add IMDB-4k dataset

dhg/data/__init__.py

@@ -18,6 +18,7 @@
 from .cora import Cora, CoraBiGraph
 from .citeseer import Citeseer, CiteseerBiGraph
 from .pubmed import Pubmed, PubmedBiGraph
+from .imdb import IMDB4k
 
 __all__ = [
     "BaseData",
@@ -48,4 +49,5 @@
     "WalmartTrips",
     "HouseCommittees",
     "News20",
+    "IMDB4k",
 ]

dhg/data/dblp.py

@@ -32,7 +32,7 @@ def __init__(self, data_root: Optional[str] = None) -> None:
 
 
 class DBLP4k(BaseData):
-    r"""The DBLP-4k dataset is a citation network hypergraph dataset for node classification task.
+    r"""The DBLP-4k dataset is a citation network dataset for node classification task.
     The dataset is an academic network from four research areas. There are 14,475 authors, 
     14,376 papers, and 20 conferences, among which 4,057 authors, 20 conferences and 
     100 papers are labeled with one of the four research areas (database, data mining, machine learning, and information retrieval). 
@@ -49,12 +49,15 @@ class DBLP4k(BaseData):
     - ``dim_features``: The dimension of author features: :math:`334`.
     - ``features``: The author feature matrix. ``torch.Tensor`` with size :math:`(4,057 \times 334)`.
     - ``labels``: The label list. ``torch.LongTensor`` with size :math:`(4,057, )`.
-    - ``edge_by_paper``: The hyperedge list constructed by the co-paper correlation. ``List`` with length :math:`(14,328 \times 2)`.
-    - ``edge_by_term``: The hyperedge list constructed by the co-term correlation. ``List`` with length :math:`(7,723 \times 2)`.
-    - ``edge_by_conf``: The hyperedge list constructed by the co-conference correlation. ``List`` with length :math:`(20 \times 2)`.
+    - ``edge_by_paper``: The hyperedge list constructed by the co-paper correlation. ``List`` with length :math:`(14,328)`.
+    - ``edge_by_term``: The hyperedge list constructed by the co-term correlation. ``List`` with length :math:`(7,723)`.
+    - ``edge_by_conf``: The hyperedge list constructed by the co-conference correlation. ``List`` with length :math:`(20)`.
     - ``paper_author_dict``: The dictionary of ``{paper_id: [author_id, ...]}``. ``Dict`` with length :math:`(14,328)`.
     - ``term_paper_dict``: The dictionary of ``{term_id: [paper_id, ...]}``. ``Dict`` with length :math:`(7,723)`.
     - ``conf_paper_dict``: The dictionary of ``{conf_id: [paper_id, ...]}``. ``Dict`` with length :math:`(20)`.
+    
+    Args:
+        ``data_root`` (``str``, optional): The ``data_root`` has stored the data. If set to ``None``, this function will auto-download from server and save into the default direction ``~/.dhg/datasets/``. Defaults to None.
     """
 
     def __init__(self, data_root: Optional[str] = None):

dhg/data/imdb.py

@@ -0,0 +1,60 @@
+from typing import Optional
+from functools import partial
+
+from dhg.datapipe import load_from_pickle, norm_ft, to_bool_tensor, to_tensor, to_long_tensor
+
+from .base import BaseData
+
+
+class IMDB4k(BaseData):
+    r"""The IMDB-4k dataset is a movie dataset for node classification task. 
+    The dataset is an online database about movies and television programs, including information such as cast, production crew, and plot summaries. 
+    This is a subset of IMDB scraped from online, containing 4278 movies, 2081 directors, and 5257 actors after data preprocessing. 
+    Movies are labeled as one of three classes (Action, Comedy, and Drama) based on their genre information. 
+    Each movie is also described by a bag-of-words representation of its plot keywords. 
+    The vertice denotes author, and two types of correlation (co-director, co-actor) can be used for building hyperedges.
+    More details see the `MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding <https://arxiv.org/pdf/2002.01680.pdf>`_ paper.
+    
+    The content of the IMDB-4k dataset includes the following:
+
+    - ``num_classes``: The number of classes: :math:`3`.
+    - ``num_vertices``: The number of vertices: :math:`4,278`.
+    - ``num_director_edges``: The number of hyperedges constructed by the co-director correlation: :math:`2,081`.
+    - ``num_actor_edges``: The number of hyperedges constructed by the co-actor correlation: :math:`5,257`.
+    - ``dim_features``: The dimension of movie features: :math:`3,066`.
+    - ``features``: The movie feature matrix. ``torch.Tensor`` with size :math:`(4,278 \times 3,066)`.
+    - ``labels``: The label list. ``torch.LongTensor`` with size :math:`(4,278, )`.
+    - ``edge_by_director``: The hyperedge list constructed by the co-director correlation. ``List`` with length :math:`(2,081)`.
+    - ``edge_by_actor``: The hyperedge list constructed by the co-actor correlation. ``List`` with length :math:`(5,257)`.
+    
+    Args:
+        ``data_root`` (``str``, optional): The ``data_root`` has stored the data. If set to ``None``, this function will auto-download from server and save into the default direction ``~/.dhg/datasets/``. Defaults to None.
+    """
+
+    def __init__(self, data_root: Optional[str] = None):
+        super().__init__("imdb_4k", data_root)
+        self._content = {
+            'num_classes': 3,
+            'num_vertices': 4278,
+            'num_director_edges': 2081,
+            'num_actor_edges': 5257,
+            'dim_features': 3066,
+            "features": {
+                "upon": [{"filename": "features.pkl", "md5": "b9cca982d3d5066ddb2013951939c070"}],
+                "loader": load_from_pickle,
+                "preprocess": [to_tensor, partial(norm_ft, ord=1)],
+            },
+            'labels': {
+                'upon': [{'filename': 'labels.pkl', 'md5': 'a45e5af53d5475ac87f5d8aa779b3a20'}],
+                'loader': load_from_pickle,
+                'preprocess': [to_long_tensor]
+            },
+            'edge_by_director': {
+                'upon': [{'filename': 'edge_by_director.pkl', 'md5': '671b7c2010e8604f037523738323cd78'}],
+                'loader': load_from_pickle,
+            },
+            'edge_by_actor': {
+                'upon': [{'filename': 'edge_by_actor.pkl', 'md5': 'dff7557861445de77b05d6215746c9f1'}],
+                'loader': load_from_pickle,
+            },
+        }