Connecting Users With Similar Tendencies in Social Networks by Weighted Random Walking on Heterogeneous Information Network

The rapid development of Internet technology has made social networks central platforms for information dissemination and acquisition. As key participants, users exhibit increasingly complex connection patterns, reflecting the dynamic nature of online interactions. Therefore, in order to better understand and manage social networks, analyzing these connection patterns, particularly identifying the potential connections between users with similar tendencies, has become a critical research focus in social network studies. Nevertheless, the existing methods exhibit limitations in comprehensively harnessing the heterogeneous nature of social networks and usually over-rely on local network structures while neglecting global patterns. To address these problems, we propose an innovative method based on weighted random walks within heterogeneous information networks (HINs). We first employ HINs to structurally represent and systematically organize complex social network data, leveraging meta-paths to model user connection patterns at semantic levels. Then, based on the meta-paths, we develop an adaptive weighted random walk strategy to integrate global structural features with local semantic information and connect users with similar tendencies. Experimental results on both Twitter and public HIN datasets demonstrate that our method outperforms other classical methods in accurately connecting users with similar tendencies.