Cross-Network Community Sensing for Anchor Link Prediction

Anchor link prediction focuses on finding accounts related to the same natural person in different online platforms and can benefit several services like user modeling and cross-network recommendation systems. Recently, community structure information has attracted interest from researchers, while most of existing methods only treat community structure information as additional information and lack utilizing the interaction between communities. To address these limitations, we propose a cross-network community sensing model for anchor link prediction (CCALP). Our CCALP model regards communities as special nodes in each network and further models community level inter-network relationships by existing anchor links. We further utilize an attention mechanism to integrate cross-network community neighbors' vectors. This attention mechanism can help us balance the degree of utilization of community knowledge. Through extensive experiments on two real-world datasets, we demonstrate that CCALP outperforms the existing baseline approaches.