Detecting Community Evolution by Utilizing Individual Temporal Semantics in Social Networks

Abstract

Social networks are becoming increasingly popular and significant. One of the most distinctive features of these networks is their dynamic nature, which means that they change over time. Consequently, the community structure on these platforms also changes with time, making the detection of community structure a crucial area of research. Specifically, there is still a lack of understanding of how social networks and communities evolve over time. In this paper, we reveal that individual changing topics (i.e., individual temporal semantics) are a vital factor that drives community evolution. A novel dynamic community detection model is proposed, which takes into account natural evolutionary features. The model first partitions social networks into snapshots. It then detects the community structure at each snapshot by utilizing individual changing topics and information from the previous snapshot. Finally, the evolution of users’ interested topic distributions and topic distributions of communities are identified. The model is compared with five state-of-the-art baselines on two real datasets, and the experimental results demonstrate that our model outperforms all baselines.

Keywords

• Community detection
• social network
• graphical model
• community evolution
• topic model