Merge pull request #301 from FluxML/doc

Add GraphSAGE to doc

Author: yuehhua

docs/bibliography.bib

@@ -171,3 +171,15 @@ @article{Wang2019
    url = {https://dl.acm.org/doi/10.1145/3326362},
    year = {2019},
 }
+
+@inproceedings{Hamilton2017,
+   abstract = {Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions.},
+   author = {William L Hamilton and Rex Ying and Jure Leskovec},
+   city = {Red Hook, NY, USA},
+   isbn = {9781510860964},
+   booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},
+   pages = {1025-1035},
+   publisher = {Curran Associates Inc.},
+   title = {Inductive Representation Learning on Large Graphs},
+   year = {2017},
+}

docs/make.jl

@@ -42,8 +42,8 @@ makedocs(
              "Dynamic Graph Update" => "dynamicgraph.md",
              "Manual" => [
                "FeaturedGraph" => "manual/featuredgraph.md",
-               "Convolutional Layers" => "manual/conv.md",
-               "Pooling Layers" => "manual/pool.md",
+               "Graph Convolutional Layers" => "manual/conv.md",
+               "Graph Pooling Layers" => "manual/pool.md",
                "Embeddings" => "manual/embedding.md",
                "Models" => "manual/models.md",
                "Linear Algebra" => "manual/linalg.md",

docs/src/manual/conv.md

@@ -1,4 +1,4 @@
-# Convolution Layers
+# Graph Convolutional Layers
 
 ## Graph Convolutional Layer
 
@@ -148,3 +148,23 @@ CGConv
 ```
 
 Reference: [Xie2018](@cite)
+
+---
+
+## SAmple and aggreGatE (GraphSAGE) Network
+
+```math
+\hat{\textbf{x}}_j = sample(\textbf{x}_j), \forall j \in \mathcal{N}(i) \\
+\textbf{m}_i = aggregate(\hat{\textbf{x}}_j) \\
+\textbf{x}_i' = \sigma (\Theta_1 \textbf{x}_i + \Theta_2 \textbf{m}_i)
+```
+
+```@docs
+SAGEConv
+MeanAggregator
+MeanPoolAggregator
+MaxPoolAggregator
+LSTMAggregator
+```
+
+Reference: [Hamilton2017](@cite) and [GraphSAGE website](http://snap.stanford.edu/graphsage/)

docs/src/manual/pool.md

@@ -1,4 +1,4 @@
-# Pooling Layers
+# Graph Pooling Layers
 
 ## Global Pooling Layer