Abstract
Social media provides a rich source of networked data. This data is represented by a set of nodes and a set of relations (edges). It is often possible to obtain or infer multiple types of relations from the same set of nodes, such as observed friend connections, inferred links via semantic comparison, or relations based off of geographic proximity. These edge sets can be represented by one multi-layer network. In this paper we review a method to perform community detection of multi-layer networks, and illustrate its use as a visualization tool for analyzing different community partitions. The algorithm is illustrated on a dataset from Twitter, specifically regarding the National Football League (NFL).
This work was partially supported by ARO under grant #W911NF-12-1-0443. We are grateful to Qiaozhu Mei who provided the Twitter data stream through his API gardenhose level access.
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Oselio, B., Kulesza, A., Hero, A. (2015). Socio-Spatial Pareto Frontiers of Twitter Networks. In: Agarwal, N., Xu, K., Osgood, N. (eds) Social Computing, Behavioral-Cultural Modeling, and Prediction. SBP 2015. Lecture Notes in Computer Science(), vol 9021. Springer, Cham. https://doi.org/10.1007/978-3-319-16268-3_48
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DOI: https://doi.org/10.1007/978-3-319-16268-3_48
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