@@ -30,7 +30,7 @@ The newly released PGL supports heterogeneous graph learning on both walk based
## Highlight: Efficiency - Support Scatter-Gather and LodTensor Message Passing
One of the most important benefits of graph neural networks compared to other models is the ability to use node-to-node connectivity information, but coding the communication between nodes is very cumbersome. At PGL we adopt **Message Passing Paradigm** similar to [DGL](https://github.com/dmlc/dgl) to help to build a customize graph neural network easily. Users only need to write ```send``` and ```recv``` functions to easily implement a simple GCN. As shown in the following figure, for the first step the send function is defined on the edges of the graph, and the user can customize the send function  to send the message from the source to the target node. For the second step, the recv function  is responsible for aggregating  messages together from different sources.
One of the most important benefits of graph neural networks compared to other models is the ability to use node-to-node connectivity information, but coding the communication between nodes is very cumbersome. At PGL we adopt **Message Passing Paradigm** similar to [DGL](https://github.com/dmlc/dgl) to help to build a customize graph neural network easily. Users only need to write ```send``` and ```recv``` functions to easily implement a simple GCN. As shown in the following figure, for the first step the send function is defined on the edges of the graph, and the user can customize the send function  to send the message from the source to the target node. For the second step, the recv function  is responsible for aggregating  messages together from different sources.
