Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Dynamic social and information networks are rapidly evolving structures where nodes and links change over time. Examples include online social media platforms, citation networks, communication graphs, and knowledge networks. Mining such networks is challenging because traditional static network analysis fails to capture temporal behavior, evolving communities, and dynamic influence patterns. This paper presents a comprehensive review of mining techniques for dynamic social and information networks, including temporal graph modeling, community evolution detection, link prediction, influence analysis, and anomaly detection. We discuss key algorithms, evaluation metrics, datasets, and applications in real-world scenarios such as social media analysis, recommendation systems, fraud detection, and information diffusion modeling. Challenges like scalability, noise, privacy, and interpretability are also highlighted. The review concludes with emerging research directions including deep dynamic graph learning and real-time network mining.

Keywords: Dynamic networks, social network mining, temporal graphs, community evolution, link prediction, information diffusion, dynamic graph learning