Abstract
Community detection in social networks is a well-studied problem. A community in social network is commonly defined as a group of people whose interactions within the group are more than outside the group. It is believed that people’s behaviour can be linked to the behaviour of their social neighbourhood. While shared characteristics of communities have been used to validate the communities found, to the best of authors’ knowledge, it is not demonstrated in the literature that communities found using social interaction data are like-minded, i.e., they behave similarly in terms of their interest in items (e.g., movie, products). In this paper, we propose a method for finding communities wherein like-mindedness is an explicit objective. We find small tight groups with many shared interests using a frequent item set mining approach and use these as building blocks for the core of these like-minded communities. We show that these communities have higher similarity in their interests compared to communities found using only the interaction information. We also compare our method against a baseline where the weight of edges are defined based on similarity in interests between nodes and show that our approach achieves far higher level of like-mindedness amongst the communities compared to this baseline as well.
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Modani, N., Gupta, R., Nagar, S., Shannigrahi, S., Goyal, S., Dey, K. (2012). Like-Minded Communities: Bringing the Familiarity and Similarity together. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds) Web Information Systems Engineering - WISE 2012. WISE 2012. Lecture Notes in Computer Science, vol 7651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35063-4_28
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DOI: https://doi.org/10.1007/978-3-642-35063-4_28
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