Datasets:
| {"title": "Community Detection in Social Network using Temporal Data", "year": 2019, "venue_abbr": "AJS", "venue_name": "American Journal of Sociology", "authors": ["Victor Stany Rozario", "A.Z.M. Ehtesham Chowdhury", "Muhammad Sarwar Jahan\n Morshed"], "abstract": " Social networks facilitate the social space where actors or the users have\nties among them. The ties and their patterns are based on their life styles and\ncommunication. Similarly, in online social media networks like Facebook,\nTwitter, Myspace etc., multiple users belong to multiple specific communities.\nThe social network represents large real-world directed social graphs.\nDetection of communities or clusters from these graphs is a problem of\nconsiderable research interest. The communities are formed using the\nneighboring nodes that have common edges and common attributes. Most of the\nexisting community detection algorithms usually consider node contents to\nanalyze the attributes of community. Some algorithms use the links between the\nnodes to determine the dense regions in the graph. But utilizing both the edge\ncontent and the vertex content to detect community are yet to be considered and\nverified, since the traditional extraction methods of vertex and edge data do\nnot consider the connectivity among the nodes. This paper presents an\nInterlinked Spatial Clustering Model (ILSCM) which provides relevant content\nselection and extraction of the temporal topics for identifying the betweenness\namong the nodes based on the context keys to detect community.\n", "doi": null, "arxiv_id": "1904.05291", "pdf_urls": ["https://arxiv.org/pdf/1904.05291"], "github": [], "is_oa": true, "sources": ["open-arxiv"], "type": "journal", "journal_ref": "https://ajse.aiub.edu/Portals/0/Users/Volume%2016%20Issue%203/2_AJSE_1642_Final.pdf", "categories": "cs.SI physics.soc-ph"} | |