GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs

Marios Papachristou, Rishab Goel, Frank Portman, Matthew Miller, Rong Jin

Keywords: graph learning, node classification, homophily, heterophily, positional embeddings, knowledge graph embeddings, monophily, label propagation

TL;DR: Scalable method for node classification for homophilous and heterophilous graphs

Abstract: In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well on homophilous graphs and are leveraged by GCNs and GATs. Such architectures, however, cannot easily scale to large real-world graphs. On the other hand, shallow (or node-level) models using ego features and adjacency embeddings work well in heterophilous graphs. In this work, we propose a novel scalable shallow method -- GLINKX -- that can work both on homophilous and heterophilous graphs. GLINKX leverages (i) novel monophilous label propagations (ii) ego/node features, (iii) knowledge graph embeddings as positional embeddings, (iv) node-level training, and (v) low-dimensional message passing. Formally, we prove novel error bounds and justify the components of GLINKX. Experimentally, we show its effectiveness of it on several homophilous and heterophilous datasets.

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