Tail-GNN: Tail-node graph neural networks

Publication Type

Conference Proceeding Article

Publication Date

8-2021

Abstract

The prevalence of graph structures in real-world scenarios enables important tasks such as node classification and link prediction. Graphs in many domains follow a long-tailed distribution in their node degrees, i.e., a significant fraction of nodes are tail nodes with a small degree. Although recent graph neural networks (GNNs) can learn powerful node representations, they treat all nodes uniformly and are not tailored to the large group of tail nodes. In particular, there is limited structural information (i.e., links) on tail nodes, resulting in inferior performance. Toward robust tail node embedding, in this paper we propose a novel graph neural network called Tail-GNN. It hinges on the novel concept of transferable neighborhood translation, to model the variable ties between a target node and its neighbors. On one hand, Tail-GNN learns a neighborhood translation from the structurally rich head nodes (i.e., high-degree nodes), which can be further transferred to the structurally limited tail nodes to enhance their representations. On the other hand, the ties with the neighbors are variable across different parts of the graph, and a global neighborhood translation is inflexible. Thus, we devise a node-wise adaptation to localize the global translation w.r.t. each node. Extensive experiments on five benchmark datasets demonstrate that our proposed Tail-GNN significantly outperforms the state-of-the-art baselines.

Keywords

Graph neural networks, tail node embedding, transferable neighborhood translation

Discipline

Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore, August 14-18

First Page

1109

Last Page

1119

Identifier

10.1145/3447548.3467276

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3447548.3467276

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