Inductive Representation Learning on Large Graphs

Code

• github.com/williamleif/GraphSAGE
  Officialtf★ 0
• github.com/massquantity/LibRecommender
  tf★ 474
• github.com/IllinoisGraphBenchmark/IGB-Datasets
  pytorch★ 86
• github.com/zxhhh97/ABot
  pytorch★ 18
• github.com/isotlaboratory/ml4vrp
  pytorch★ 16
• github.com/weiyinwei/huign
  pytorch★ 10
• github.com/chatterjeeayan/upna
  pytorch★ 1
• github.com/erfmah/answering_graph_queries
  pytorch★ 1
• github.com/weiyinwei/mmgcn
  pytorch★ 0
• github.com/qema/orca-py
  pytorch★ 0

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.

Tasks

Benchmark Results

Reproductions