BANANA: when Behavior ANAlysis meets social Network Alignment
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 the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end to end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover's distance based alignment component to fuse users' behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network component to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end to end manner. Through extensive experiments on real world datasets, we demonstrate that our proposed approach outperforms the state of the art methods in the social network alignment task and the user behavior analysis task, respectively.
IJCAI