GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation

• URL: http://arxiv.org/abs/2009.05266v3
• Date: Thu, 4 May 2023 03:01:49 GMT
• Title: GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation
• Authors: Siyue Xie, Yiming Li, Da Sun Handason Tam, Xiaxin Liu, Qiu Fang Ying, Wing Cheong Lau, Dah Ming Chiu, Shou Zhi Chen
• Abstract summary: We propose the Graph Temporal Edge Aggregation framework for inductive learning on Temporal Interaction Graphs (TIGs) By aggregating features of neighboring nodes and the corresponding edge embeddings, GTEA jointly learns both topological and temporal dependencies of a TIG. In addition, a sparsity-inducing self-attention scheme is incorporated for neighbor aggregation, which highlights more important neighbors and suppresses trivial noises for GTEA.
• Score: 11.526912398475513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose the Graph Temporal Edge Aggregation (GTEA) framework for inductive learning on Temporal Interaction Graphs (TIGs). Different from previous works, GTEA models the temporal dynamics of interaction sequences in the continuous-time space and simultaneously takes advantage of both rich node and edge/ interaction attributes in the graph. Concretely, we integrate a sequence model with a time encoder to learn pairwise interactional dynamics between two adjacent nodes.This helps capture complex temporal interactional patterns of a node pair along the history, which generates edge embeddings that can be fed into a GNN backbone. By aggregating features of neighboring nodes and the corresponding edge embeddings, GTEA jointly learns both topological and temporal dependencies of a TIG. In addition, a sparsity-inducing self-attention scheme is incorporated for neighbor aggregation, which highlights more important neighbors and suppresses trivial noises for GTEA. By jointly optimizing the sequence model and the GNN backbone, GTEA learns more comprehensive node representations capturing both temporal and graph structural characteristics. Extensive experiments on five large-scale real-world datasets demonstrate the superiority of GTEA over other inductive models.

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