A multilevel clustering technique for community detection
| العنوان: | A multilevel clustering technique for community detection |
|---|---|
| المؤلفون: | Mark Liptrott, Ioannis Korkontzelos, Isa Inuwa-Dutse |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Social and Information Networks (cs.SI), FOS: Computer and information sciences, 0209 industrial biotechnology, Computer Science - Machine Learning, Social network, Point (typography), business.industry, Computer science, Cognitive Neuroscience, Computer Science - Social and Information Networks, 02 engineering and technology, Data science, Machine Learning (cs.LG), Computer Science Applications, Unit (housing), 020901 industrial engineering & automation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, Social media, Dimension (data warehouse), Cluster analysis, business |
| الوصف: | 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. 32 pages, 8 figures, journal article |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a298bc1b6b671f645ce778c0af3ae96f http://arxiv.org/abs/2101.06551 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....a298bc1b6b671f645ce778c0af3ae96f |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |