Abstract
Community detection is one of the most topics that are covered by social network analysis researchers. Early works focused mainly on partitioning networks into several global communities before they target communities evolution over time. The main drawback of such approach is the difficulty to obtain the entire network on which we may process a set of algorithms to track evolution. As a result, many researchers focused on detecting and tracking local dynamic communities.
Basically, detecting and tracking local dynamic communities is a process that moves from a set of nodes to build community structure followed by a structure evolution tracking. A particular type of local communities is called “ego-communities”. Unlike local communities, ego-centered ones have the advantage of being able to expand the community according to the neighborhood step of interest node. They allow, among others, to better identify and track the network elements activities.
Existing ego-community detection algorithms do not support directed networks and are designed for static networks. This failure led us to propose a new solution allowing to detect and track ego-community evolution in directed, weighted and dynamic social networks. We present some illustrative examples to explain the working principle of our solution.
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Notes
- 1.
To select the ego’s neighbors, we proposed a new metric allowing to classify the nodes based on the number of their neighbors in ego-community as well as on their weights of adjacent links. This metric is detailed in another paper.
References
Danisch, M.: Mesures de proximité appliquées à la détection de communautés dans les grands graphes de terrain. Ph.D. thesis, Paris 6 (2015)
Hou, J., Yu, Z., Hong, X., Wang, L.: Social circle detection based on micro-blogging topic and user follow relationship. In: 2016 International Conference on Asian Language Processing (IALP), pp. 222–227. IEEE (2016)
Lan, C., Yang, Y., Li, X., Luo, B., Huan, J.: Learning social circles in ego-networks based on multi-view network structure. IEEE Trans. Knowl. Data Eng. 29(8), 1681–1694 (2017)
Mcauley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 4 (2014)
Moctar, A.O.M., Sarr, I.: Ego-centered community detection in directed and weighted networks. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM 2017, New York, USA, pp. 1201–1208. ACM (2017)
Rees, B.S., Gallagher, K.B.: EgoClustering: overlapping community detection via merged friendship-groups. In: Özyer, T., Rokne, J., Wagner, G., Reuser, A. (eds.) The Influence of Technology on Social Network Analysis and Mining. LNSN, vol. 6, pp. 1–20. Springer, Vienna (2013). https://doi.org/10.1007/978-3-7091-1346-2_1
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ould Mohamed Moctar, A., Sarr, I. (2018). Ego-Community Evolution Tracking in Instant Messaging Networks. In: Kebe, C., Gueye, A., Ndiaye, A., Garba, A. (eds) Innovations and Interdisciplinary Solutions for Underserved Areas. InterSol 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 249. Springer, Cham. https://doi.org/10.1007/978-3-319-98878-8_2
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DOI: https://doi.org/10.1007/978-3-319-98878-8_2
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