GRETO: REMEDYING DYNAMIC GRAPH TOPOLOGY-TASK DISCORDANCE VIA TARGET HOMOPHILY

Abstract

Dynamic graphs are ubiquitous across disciplines where observations usually change over time. Regressions on dynamic graphs often contribute to diverse critical tasks, such as climate early-warning and traffic controlling. Existing homophily Graph Neural Networks (GNNs) adopt physical connections or feature similarity as adjacent matrix to perform node-level aggregations. However, on dynamic graphs with diverse node-wise relations, exploiting a pre-defined fixed topology for message passing inevitably leads to the aggregations of targetdeviated neighbors. We designate such phenomenon as the topology-task discordance, which naturally challenges the homophily assumption. In this work, we revisit node-wise relationships and explore novel homophily measurements on dynamic graphs with both signs and distances, capturing multiple node-level spatial relations and temporal evolutions. We discover that advancing homophily aggregations to signed target-oriented message passing can effectively resolve the discordance and promote aggregation capacity. Therefore, a GReTo is proposed, which performs signed message passing in immediate neighborhood, and exploits both local environments and target awareness to realize high-order message propagation. Empirically, our solution achieves significant improvements against best baselines, notably improving 24.79% on KnowAir and 3.60% on Metr-LA.

1. INTRODUCTION

Graph-structured data mining has become a popular technique in numerous disciplines, such as social networks (You et al., 2022 ), road networks (Chen et al., 2020) , and molecule analysis (Abu-El-Haija et al., 2019) . However, existing solutions to graph mining usually make the assumption of homophily on graphs where connected nodes tend to share similar features or have the same labels (targets). Actually, in real-world graphs, the homophily assumption does not always hold on (Zhu et al., 2020) . Thus, Graph Neural Networks (GNNs) considering heterophily are proposed to break the homophily assumption, which disentangle the complex neighborhood components (Ma et al., 2019; Du et al., 2022) and model the edge diversity (Zhu et al., 2021a; Wang et al., 2022a ) by separately aggregating similar and dissimilar signals (Bo et al., 2021; Yan et al., 2021) . Despite achievements, heterophily GNNs are mostly investigated on classification tasks over static graphs while less explored on node-level regressions over dynamic graphs. Therefore, it provides an opportunity to dissect how edge-type disentanglement boosts regression capacity on dynamic graphs. Regression tasks are more challenging than classification as the latter only considers discrete labels with much tolerance (Wang et al., 2022b) . Actually, nodes in dynamic graphs are more prone to suffer complex neighborhood distributions (Ma et al., 2022) due to the existence of time-varying values and different edge types, incurring misleading message passing when aggregating target-deviated neighbors. The misleading message passing is formally designated as the topology-task discordance in our work (see Fig. 1(a) ). We take traffic volumes of road networks as an intuitive example of edge

