Abstract
Online communities emerge as a major way of delivering and sharing resources. Yet communities in social networks cannot be accurately classified due to the randomness of clustering and the insufficient use of semantics of links. In this paper, a semantic inference based community discovery model is proposed to extract multiple layers of semantics from the topological structure of node relationships and semantic connections between nodes to search and discover communities. The ego-Twitter dataset was used, which contains 81306 nodes (accounts) and 1768149 edges, to test the proposed model. Experiments show that our model is suitable for sparse networks and nodes that contain rich semantics. Especially, in terms of modularity, our model outperforms the Latent Factor Model (LFW) and K-means algorithm. Our model outperforms LFW by achieved faster speed when the scale of online community is expanded to more than 1000, which demonstrates that our model has higher efficiency with network that has abundant semantics.
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This paper is funded in part by the Capacity Development Grant of Southwest University (SWU116007).
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Liu, W., Ruan, Q., Zhang, L., Ren, W. (2022). An Improved Semantic Link Based Cyber Community Discovery Model on Social Network. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_26
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DOI: https://doi.org/10.1007/978-3-031-10989-8_26
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