Graph Representation Learning Network via Adaptive Sampling

TL;DR

This paper introduces a scalable graph neural network architecture that uses adaptive sampling and multi-step transition probabilities to improve efficiency and incorporate edge types, achieving strong results on multiple benchmarks.

Contribution

It proposes a novel, efficient graph representation learning method that combines adaptive sampling with multi-step transition attention, addressing scalability and edge-type integration.

Findings

Achieved comparable or better results on standard graph benchmarks.

Effectively incorporates different edge types in node representations.

Demonstrated scalability on large and dense graphs.

Abstract

Graph Attention Network (GAT) and GraphSAGE are neural network architectures that operate on graph-structured data and have been widely studied for link prediction and node classification. One challenge raised by GraphSAGE is how to smartly combine neighbour features based on graph structure. GAT handles this problem through attention, however the challenge with GAT is its scalability over large and dense graphs. In this work, we proposed a new architecture to address these issues that is more efficient and is capable of incorporating different edge type information. It generates node representations by attending to neighbours sampled from weighted multi-step transition probabilities. We conduct experiments on both transductive and inductive settings. Experiments achieved comparable or better results on several graph benchmarks, including the Cora, Citeseer, Pubmed, PPI, Twitter, and YouTube datasets.