A multilevel clustering technique for community detection
This work addresses the challenge of community detection in dynamic social networks, which is incremental as it builds on existing methods by incorporating textual information for improved accuracy.
The study tackled the problem of detecting cohesive communities in social networks like Twitter by proposing a multilevel clustering technique that leverages structural and textual information, with experimental evaluation on benchmark datasets demonstrating its efficacy.
A network is a composition of many communities, i.e., sets of nodes and edges with stronger relationships, with distinct and overlapping properties. Community detection is crucial for various reasons, such as serving as a functional unit of a network that captures local interactions among nodes. Communities come in various forms and types, ranging from biologically to technology-induced ones. As technology-induced communities, social media networks such as Twitter and Facebook connect a myriad of diverse users, leading to a highly connected and dynamic ecosystem. Although many algorithms have been proposed for detecting socially cohesive communities on Twitter, mining and related tasks remain challenging. This study presents a novel detection method based on a scalable framework to identify related communities in a network. We propose a multilevel clustering technique (MCT) that leverages structural and textual information to identify local communities termed microcosms. Experimental evaluation on benchmark models and datasets demonstrate the efficacy of the approach. This study contributes a new dimension for the detection of cohesive communities in social networks. The approach offers a better understanding and clarity toward describing how low-level communities evolve and behave on Twitter. From an application point of view, identifying such communities can better inform recommendation, among other benefits.