Graph Neural Point Process for Temporal Interaction Prediction

Wenwen Xia; Yuchen Li; Shenghong Li

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Graph Neural Point Process for Temporal Interaction Prediction

机译：Graph Neural Point Process for Temporal Interaction Prediction

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Temporal graphs are ubiquitous data structures in many scenarios, including social networks, user-item interaction networks, etc. In this paper, we focus on predicting the exact time of future interactions between node pairs on a temporal graph. This problem can support interesting applications including time-sensitive items recommendation, congestion prediction on road networks, etc. We present the Graph Neural Point Process (GNPP) to tackle this problem. GNPP relies on the graph neural message passing and the temporal point process framework. Most previous graph neural models devised for temporal graphs either utilize the chronological order information or rely on specific point process models, ignoring the exact timestamps and complicated temporal patterns. In GNPP, we adapt a time encoding scheme to map real-valued timestamps to a high-dimensional vector space so that the temporal information can be modeled precisely. Further, GNPP considers the structural information of graphs by conducting message passing aggregation on the constructed line graph. The obtained representation defines a neural conditional intensity function that models events’ generation mechanisms for predicting interactions’ time between node pairs. We evaluate this model on several synthetic and real-world temporal graphs where it outperforms recently proposed neural point process models and graph neural models devised for temporal graphs. We further conduct ablation comparisons and visual analyses to shed some light on the learned model and understand the functionality of important components comprehensively.

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《IEEE Transactions on Knowledge and Data Engineering》 |2023年第5期|4867-4879|共13页
作者
Wenwen Xia; Yuchen Li; Shenghong Li;
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作者单位

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China;

School of Computing and Information Systems, Singapore Management University, Singapore;

MoE Key Lab of Artificial Intelligence, AIInstitute, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China;

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正文语种英语
中图分类计算技术、计算机技术;
关键词
Message passing; Predictive models; Adaptation models; Roads; Encoding; Correlation; Computational modeling;

Graph Neural Point Process for Temporal Interaction Prediction

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