Lying Graph Convolution: Learning to Lie for Node Classification Tasks

TL;DR

Lying-GCN introduces an adaptive lying mechanism in graph neural networks, enabling improved node classification performance across heterophilic and homophilic graph structures by allowing nodes to strategically share their opinions.

Contribution

It proposes a novel lying mechanism inspired by opinion dynamics that adapts to different graph structures, enhancing node classification performance.

Findings

Improves accuracy in heterophilic graphs.

Maintains performance in homophilic graphs.

The lying mechanism is effective across synthetic and real datasets.

Abstract

In the context of machine learning for graphs, many researchers have empirically observed that Deep Graph Networks (DGNs) perform favourably on node classification tasks when the graph structure is homophilic (\ie adjacent nodes are similar). In this paper, we introduce Lying-GCN, a new DGN inspired by opinion dynamics that can adaptively work in both the heterophilic and the homophilic setting. At each layer, each agent (node) shares its own opinions (node embeddings) with its neighbours. Instead of sharing its opinion directly as in GCN, we introduce a mechanism which allows agents to lie. Such a mechanism is adaptive, thus the agents learn how and when to lie according to the task that should be solved. We provide a characterisation of our proposal in terms of dynamical systems, by studying the spectral property of the coefficient matrix of the system. While the steady state of the system collapses to zero, we believe the lying mechanism is still usable to solve node classification tasks. We empirically prove our belief on both synthetic and real-world datasets, by showing that the lying mechanism allows to increase the performances in the heterophilic setting without harming the results in the homophilic one.