[Data] Synthetic dataset for graph classificaiton

docs/source/api/python/data.rst

@@ -25,4 +25,10 @@ Stanford sentiment treebank dataset
 For more information about the dataset, see `Sentiment Analysis <https://nlp.stanford.edu/sentiment/index.html>`__.
 
 .. autoclass:: SST
-    :members: __getitem__, __len__
+    :members: __getitem__, __len__
+
+Mini graph classification dataset
+`````````````````````````````````
+
+.. autoclass:: MiniGC
+    :members: __getitem__, __len__, num_classes

python/dgl/data/__init__.py

@@ -3,6 +3,7 @@
 
 from . import citation_graph as citegrh
 from .citation_graph import CoraBinary
+from .minigc import *
 from .tree import *
 from .utils import *
 from .sbm import SBMMixture

python/dgl/data/minigc.py

@@ -0,0 +1,134 @@
+"""A mini synthetic dataset for graph classification benchmark."""
+
+from collections.abc import Sequence
+import math
+import networkx as nx
+import numpy as np
+
+from ..graph import DGLGraph
+
+__all__ = ['MiniGCDataset']
+
+class MiniGCDataset(object):
+    """The dataset class.
+
+    The datset contains 8 different types of graphs.
+    - class 0 : cycle graph
+    - class 1 : star graph
+    - class 2 : wheel graph
+    - class 3 : lollipop graph
+    - class 4 : hypercube graph
+    - class 5 : grid graph
+    - class 6 : clique graph
+    - class 7 : circular ladder graph
+    """
+    def __init__(self, num_graphs, min_num_v, max_num_v):
+        """
+        Parameters
+        ----------
+        num_graphs: int
+            Number of graphs in this dataset.
+        min_num_v: int
+            Minimum number of nodes for graphs
+        max_num_v: int
+            Maximum number of nodes for graphs
+        """
+        super(MiniGCDataset, self).__init__()
+        self.num_graphs = num_graphs
+        self.min_num_v = min_num_v
+        self.max_num_v = max_num_v
+        self.graphs = []
+        self.labels = []
+        self._generate()
+
+    def __len__(self):
+        return len(self.graphs)
+
+    def __getitem__(self, idx):
+        return self.graphs[idx], self.labels[idx]
+
+    @property
+    def num_classes(self):
+        """Number of classes."""
+        return 8
+
+    def _generate(self):
+        self._gen_cycle(self.num_graphs // 8)
+        self._gen_star(self.num_graphs // 8)
+        self._gen_wheel(self.num_graphs // 8)
+        self._gen_lollipop(self.num_graphs // 8)
+        self._gen_hypercube(self.num_graphs // 8)
+        self._gen_grid(self.num_graphs // 8)
+        self._gen_clique(self.num_graphs // 8)
+        self._gen_circular_ladder(self.num_graphs - len(self.graphs))
+        # preprocess
+        for i in range(self.num_graphs):
+            self.graphs[i] = DGLGraph(self.graphs[i])
+            # add self edges
+            nodes = self.graphs[i].nodes()
+            self.graphs[i].add_edges(nodes, nodes)
+
+    def _gen_cycle(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            g = nx.cycle_graph(num_v)
+            self.graphs.append(g)
+            self.labels.append(0)
+
+    def _gen_star(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            # nx.star_graph(N) gives a star graph with N+1 nodes
+            g = nx.star_graph(num_v - 1)
+            self.graphs.append(g)
+            self.labels.append(1)
+
+    def _gen_wheel(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            g = nx.wheel_graph(num_v)
+            self.graphs.append(g)
+            self.labels.append(2)
+
+    def _gen_lollipop(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            path_len = np.random.randint(2, num_v // 2)
+            g = nx.lollipop_graph(m=num_v - path_len, n=path_len)
+            self.graphs.append(g)
+            self.labels.append(3)
+
+    def _gen_hypercube(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            g = nx.hypercube_graph(int(math.log(num_v, 2)))
+            g = nx.convert_node_labels_to_integers(g)
+            self.graphs.append(g)
+            self.labels.append(4)
+
+    def _gen_grid(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            assert num_v >= 4, 'We require a grid graph to contain at least two ' \
+                                   'rows and two columns, thus 4 nodes, got {:d} ' \
+                                   'nodes'.format(num_v)
+            n_rows = np.random.randint(2, num_v // 2)
+            n_cols = num_v // n_rows
+            g = nx.grid_graph([n_rows, n_cols])
+            g = nx.convert_node_labels_to_integers(g)
+            self.graphs.append(g)
+            self.labels.append(5)
+
+    def _gen_clique(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            g = nx.complete_graph(num_v)
+            self.graphs.append(g)
+            self.labels.append(6)
+
+    def _gen_circular_ladder(self, n):
+        for _ in range(n):
+            num_v = np.random.randint(self.min_num_v, self.max_num_v)
+            g = nx.circular_ladder_graph(num_v)
+            self.graphs.append(g)
+            self.labels.append(7)

tests/compute/test_data.py

@@ -0,0 +1,9 @@
+import dgl.data as data
+
+def test_minigc():
+    ds = data.MiniGCDataset(16, 10, 20)
+    g, l = list(zip(*ds))
+    print(g, l)
+
+if __name__ == '__main__':
+    test_minigc()