Resisting the edge-type disturbance for link prediction in heterogeneous social networks
Abstract
The rapid development of heterogeneous networks has proposed new challenges to the long-standing link prediction problem. Existing models trained on the verified edge samples from different types usually learn type-specific knowledge, and their type-specific predictions may be contradictory for unverified edge samples with uncertain types. This challenge is termed edge-type disturbance in link prediction in heterogeneous networks. To address this challenge, we develop a disturbance-resilient prediction method (DRPM) comprising a structural characterizer, a type differentiator, and a resilient predictor. The structural characterizer is responsible for learning edge representations for link prediction. Concurrently, the type differentiator distinguishes type-specific edge representations to generate diverse type experts while maximizing their link prediction performances on specific types. Further, the resilient predictor evaluates the reliability weights of different type experts to develop a resilient prediction mechanism to aggregate discriminable predictions. Extensive experiments conducted on various real-world datasets demonstrate the importance of the explainable introduction of the edge-type disturbance and the superiority of DRPM over state-of-the-art methods.
Citation
Wang , H , Liu , R , Chen , J , Shi , C , Fang , L , Liu , S & Gong , Z 2024 , ' Resisting the edge-type disturbance for link prediction in heterogeneous social networks ' , ACM Transactions on Knowledge Discovery from Data (TKDD) , vol. 18 , no. 2 , 45 . https://doi.org/10.1145/3614099
Publication
ACM Transactions on Knowledge Discovery from Data (TKDD)
Status
Peer reviewed
ISSN
1556-4681Type
Journal article
Description
Funding: This work is supported by the National Natural Science Foundation of China (62006089), Macau Young Scholars Program, National Key D&R Program of China (2019YFB1600704), Science and Technology Development Fund, Macau SAR (0068/2020/AGJ, SKL-IOTSC(UM)-2021-2023), GDST (2020B1212030003), MYRG2022-00192-FST, National Natural Science Foundation of China (62102265), Natural Science Foundation of Guangdong Province of China (2022A1515011474), Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) (GML-KF-22-29).Collections
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