When Behavior Analysis Meets Social Network Alignment

Zhongbao Zhang

doi:10.1109/TKDE.2022.3197985

When Behavior Analysis Meets Social Network Alignment

2022-01-01

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DOI：10.1109/TKDE.2022.3197985

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Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment and user behavior analysis problem in this paper. We design a novel framework named BANANA-RGB. In this framework, to capture users' multi-scale behavior information in each social network, we train a variant of the hierarchical periodic memory network with personalized memorization. To leverage behavior analysis for social network alignment, we design a tensor fusion network-based alignment component to improve the performance. To further leverage social network alignment for behavior analysis, we design a gating-based cross-network behavior fusion component to integrate users' behavior information in different social networks based on the alignment result. We iteratively train the above two components to make the two tasks benefit from each other. Extensive experiments on real-world datasets demonstrate that our proposed approach outperforms the state-of-the-art methods.

Keywords:

Social network alignment

behavior analysis

social networks

data mining

When Behavior Analysis Meets Social Network Alignment

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