Abstract
Knowledge discovery in social networks is not a trivial task. Often research in this context uses concepts of data mining, social network analysis, trust discovery and sentiment analysis. The connected network of people is generally represented by a directed graph (social graph), whose formulation includes representing people as nodes and their relationships as edges, which also can be labeled to describe the relationship (eg. friend, son and girlfriend). This environment of connected nodes behaves like a dynamic network, whose nodes and connections are constantly being updated. People tend to communicate or relate better with other people who have a common or similar way of thinking, which generates groups of people with common interests, the communities. This paper studies the use of a graph clustering approach, the Coring Method, originally employed in image segmentation task, in order to be applied in the context of community discovery on a social network environment.
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Souza, J.G., Silva, E.M., Brito, P.F., Costa, J.A.F., Salgado, A.C., Meira, S.R.L. (2013). Using Graph Clustering for Community Discovery in Web-Based Social Networks. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_15
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DOI: https://doi.org/10.1007/978-3-642-38715-9_15
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