TIGER: Temporal Interaction Graph Embedding with Restarts
Yao Zhang, Yun Xiong, Yongxiang Liao, Yiheng Sun, Yucheng Jin, Xuehao, Zheng, Yangyong Zhu

TL;DR
TIGER is a novel temporal interaction graph embedding model that enables parallel processing and improves representation freshness by restarting at multiple timestamps with a dual memory system.
Contribution
The paper introduces TIGER, a model allowing restarts at any timestamp and parallelization, with a dual memory module to enhance neighborhood information utilization.
Findings
TIGER achieves superior accuracy on multiple datasets.
TIGER significantly reduces training time through parallelization.
The dual memory module alleviates staleness in node representations.
Abstract
Temporal interaction graphs (TIGs), consisting of sequences of timestamped interaction events, are prevalent in fields like e-commerce and social networks. To better learn dynamic node embeddings that vary over time, researchers have proposed a series of temporal graph neural networks for TIGs. However, due to the entangled temporal and structural dependencies, existing methods have to process the sequence of events chronologically and consecutively to ensure node representations are up-to-date. This prevents existing models from parallelization and reduces their flexibility in industrial applications. To tackle the above challenge, in this paper, we propose TIGER, a TIG embedding model that can restart at any timestamp. We introduce a restarter module that generates surrogate representations acting as the warm initialization of node representations. By restarting from multiple…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques · Graph Theory and Algorithms
