Abstract:Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overlook the potential causal influences of node features on edge features, leading to a loss of relevant prior information. In this work, we present an empirical exploration using the Causal Edge Classification Framework (CECF). Unlike conventional causal inference methods, CECF is the first framework to apply causal inference principles to the edge classification task and to explore modeling edge features as a high-dimensional treatment within a causal framework. Based on the node embedding of Graph Neural Network (GNN), CECF seeks to learn a balanced representation of high-dimensional edge features by mitigating the potential influence of node features. Then, a cross-attention network captures the complex dependencies between node and edge features for final edge this http URL experiments demonstrate that CECF not only achieves superior performance but also serves as a flexible, plug-and-play enhancement for existing this http URL also provide empirical analyses, offering insights into when and how this high-dimensional causal modeling framework works for the edge classification.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.00374 [cs.LG] |
| (or arXiv:2605.00374v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.00374 arXiv-issued DOI via DataCite (pending registration) |
From: Duanyu Feng [view email]
[v1]
Fri, 1 May 2026 03:40:12 UTC (780 KB)
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