International Journal of Crowd Science 2022, 6(2): 80-91 https://doi.org/10.26599/IJCS.2022.9100013

Open Access | Issue | Published: 30 June 2022

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

Show Author's Information Hide Author's Information Jiarun Zhu^¹, Xingyu Wu^¹, Muhammad Usman^¹, Xiangyu Wang^¹, Huanhuan Chen^¹(

)

1 School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China

Keywords:

link prediction, heterogeneous graph, attention network, continuous-time model

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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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Abstract Full text About this article

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.

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

Show Author's information Hide Author's Information Jiarun Zhu^¹, Xingyu Wu^¹, Muhammad Usman^¹, Xiangyu Wang^¹, Huanhuan Chen^¹(

)

1 School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China

Abstract

Keywords: link prediction, heterogeneous graph, attention network, continuous-time model

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Received: 28 January 2022

Revised: 05 April 2022

Accepted: 06 April 2022

Published: 30 June 2022

Issue date: June 2022

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This work was supported by National Natural Science Foundation of China (Nos. 62176245 and 62137002), Anhui Province (Nos. 202104a05020011 and 202103a07020002), and the Fundamental Research Funds for the Central Universities.

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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/).