Merge pull request #90 from jermainewang/cpp

[GraphIndex] Graph index and many related changes

examples/pytorch/gcn/README.md

@@ -4,43 +4,9 @@ Graph Convolutional Networks (GCN)
 Paper link: [https://arxiv.org/abs/1609.02907](https://arxiv.org/abs/1609.02907)
 Author's code repo: [https://github.com/tkipf/gcn](https://github.com/tkipf/gcn)
 
-The folder contains three different implementations using DGL.
+The folder contains two different implementations using DGL.
 
-Naive GCN (gcn.py)
--------
-The model is defined in the finest granularity (aka on *one* edge and *one* node).
-
-* The message function `gcn_msg` computes the message for one edge. It simply returns the `h` representation of the source node.
-  ```python
-  def gcn_msg(src, edge):
-    # src['h'] is a tensor of shape (D,). D is the feature length.
-    return src['h']
-  ```
-* The reduce function `gcn_reduce` accumulates the incoming messages for one node. The `msgs` argument is a list of all the messages. In GCN, the incoming messages are summed up.
-  ```python
-  def gcn_reduce(node, msgs):
-    # msgs is a list of in-coming messages.
-    return sum(msgs)
-  ```
-* The update function `NodeUpdateModule` computes the new new node representation `h` using non-linear transformation on the reduced messages.
-  ```python
-  class NodeUpdateModule(nn.Module):
-    def __init__(self, in_feats, out_feats, activation=None):
-      super(NodeUpdateModule, self).__init__()
-      self.linear = nn.Linear(in_feats, out_feats)
-      self.activation = activation
-
-    def forward(self, node, accum):
-      # accum is a tensor of shape (D,).
-      h = self.linear(accum)
-      if self.activation:
-          h = self.activation(h)
-      return {'h' : h}
-  ```
-
-After defining the functions on each node/edge, the message passing is triggered by calling `update_all` on the DGLGraph object (in GCN module).
-
-Batched GCN (gcn_batch.py)
+Batched GCN (gcn.py)
 -----------
 Defining the model on only one node and edge makes it hard to fully utilize GPUs. As a result, we allow users to define model on a *batch of* nodes and edges.
 

examples/pytorch/gcn/gcn_spmv.py

@@ -23,10 +23,10 @@ class NodeApplyModule(nn.Module):
         self.activation = activation
 
     def forward(self, node):
-        h = self.linear(node)
+        h = self.linear(node['h'])
         if self.activation:
             h = self.activation(h)
-        return h
+        return {'h' : h}
 
 class GCN(nn.Module):
     def __init__(self,
@@ -49,14 +49,16 @@ class GCN(nn.Module):
         self.layers.append(NodeApplyModule(n_hidden, n_classes))
 
     def forward(self, features):
-        self.g.set_n_repr(features)
+        self.g.set_n_repr({'h' : features})
         for layer in self.layers:
             # apply dropout
             if self.dropout:
-                val = F.dropout(self.g.get_n_repr(), p=self.dropout)
-                self.g.set_n_repr(val)
-            self.g.update_all(fn.copy_src(), fn.sum(), layer, batchable=True)
-        return self.g.pop_n_repr()
+                g.apply_nodes(apply_node_func=
+                        lambda node: F.dropout(node['h'], p=self.dropout))
+            self.g.update_all(fn.copy_src(src='h', out='m'),
+                              fn.sum(msg='m', out='h'),
+                              layer)
+        return self.g.pop_n_repr('h')
 
 def main(args):
     # load and preprocess dataset

examples/pytorch/line_graph/README.md

+Community Detection with Graph Neural Networks (CDGNN)
+============
+
+Paper link: [https://arxiv.org/abs/1705.08415](https://arxiv.org/abs/1705.08415)
+
+Author's code repo: [https://github.com/joanbruna/GNN_community](https://github.com/joanbruna/GNN_community)
+
+This folder contains a DGL implementation of the CDGNN model.
+
+An experiment on the Stochastic Block Model in default settings can be run with
+
+```bash
+python train.py
+```
+
+An experiment on the Stochastic Block Model in customized settings can be run with
+```bash
+python train.py --batch-size BATCH_SIZE --gpu GPU --n-communities N_COMMUNITIES --n-features N_FEATURES --n-graphs N_GRAPH --n-iterations N_ITERATIONS --n-layers N_LAYER --n-nodes N_NODE --model-path MODEL_PATH --radius RADIUS
+```

examples/pytorch/line_graph/data.py

-import torch.utils as utils

examples/pytorch/line_graph/utils.py

+def cycle(loader):
+    while True:
+        for x in loader:
+            yield x