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Open Access

Link Prediction in Continuous-Time Dynamic Heterogeneous Graphs with Causality of Event Types

Jiarun Zhu1, Xingyu Wu1, Muhammad Usman1, Xiangyu Wang1, Huanhuan Chen1( )
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
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Abstract

Dynamic heterogeneous graphs comprise different types of events with temporal labels. In many real-world scenarios, the temporal order of different types of events possibly implies causal relationships between these event types. However, existing methods designed to model dynamic heterogeneous graphs neglect the underlying causal relationships between event types. For instance, the determination of the occurrence of a new event is misled by irrelevant historical events considering the type and could lead to performance degradation. First, this paper explicitly defines the causality of event types by the heterogeneous causality graph to utilize such causality from the perspective of the graph structure to tackle the aforementioned issue. Second, this paper proposes the event type causality based continuous-time heterogeneous attention network (ECHN) to model dynamic heterogeneous graphs. ECHN aggregates features based on the strength of different causal relationships between event types in the prediction process to utilize the causality of event types from the perspective of the modeling algorithm. The utilities of event type causality weaken the negative effect of irrelevant events. Experimental results demonstrate that ECHN outperforms state-of-the-arts in the link prediction task. The authors believe that this paper is the first study to model the causality of event types in dynamic heterogeneous graphs explicitly.

Keywords

heterogeneous graphattention networklink predictioncontinuous-time model

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International Journal of Crowd Science
Volume 6 Issue 2,
June 2022
Pages 80-91
DOI: 10.26599/IJCS.2022.9100013
Cite this article:
Zhu J, Wu X, Usman M, et al. Link Prediction in Continuous-Time Dynamic Heterogeneous Graphs with Causality of Event Types. International Journal of Crowd Science, 2022, 6(2): 80-91. https://doi.org/10.26599/IJCS.2022.9100013

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Received: 28 January 2022
Revised: 05 April 2022
Accepted: 06 April 2022
Published: 30 June 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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