Significant Ties Graph Neural Networks for Continuous-Time Temporal Networks Modeling

Autor:	Wu, Jiayun, Jia, Tao, Wang, Yansong, Tao, Li
Rok vydání:	2022
Předmět:	Computer Science - Social and Information Networks Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Temporal networks are suitable for modeling complex evolving systems. It has a wide range of applications, such as social network analysis, recommender systems, and epidemiology. Recently, modeling such dynamic systems has drawn great attention in many domains. However, most existing approaches resort to taking discrete snapshots of the temporal networks and modeling all events with equal importance. This paper proposes Significant Ties Graph Neural Networks (STGNN), a novel framework that captures and describes significant ties. To better model the diversity of interactions, STGNN introduces a novel aggregation mechanism to organize the most significant historical neighbors' information and adaptively obtain the significance of node pairs. Experimental results on four real networks demonstrate the effectiveness of the proposed framework. Comment: 9 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.06590 Zobrazit plný text záznamu View this record from Arxiv