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Title: Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
Edges in many real-world social/information networks are associated with rich text information (e.g., user-user communications or user-product reviews). However, mainstream network representation learning models focus on propagating and aggregating node attributes, lacking specific designs to utilize text semantics on edges. While there exist edge-aware graph neural networks, they directly initialize edge attributes as a feature vector, which cannot fully capture the contextualized text semantics of edges. In this paper, we propose Edgeformers, a framework built upon graph-enhanced Transformers, to perform edge and node representation learning by modeling texts on edges in a contextualized way. Specifically, in edge representation learning, we inject network information into each Transformer layer when encoding edge texts; in node representation learning, we aggregate edge representations through an attention mechanism within each node’s ego-graph. On five public datasets from three different domains, Edgeformers consistently outperform state-of-the-art baselines in edge classification and link prediction, demonstrating the efficacy in learning edge and node representations, respectively.  more » « less
Award ID(s):
1741317 1956151 1704532
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Date Published:
Journal Name:
The Eleventh International Conference on Learning Representations, {ICLR} 2023
Subject(s) / Keyword(s):
["Edgeformers","Representation Learning","Textual-Rich Networks","Transformers","Machine Learning"]
Medium: X
Kigali, Rwanda
Sponsoring Org:
National Science Foundation
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