Abstract
As social media becomes more feature-rich and the capability for interactions between users becomes more complex as a result, it may become necessary to expand the models used in data analysis to represent more complex interactions and networks. To that effect, researchers have begun using graphs with different types of vertices or even hyperedges to represent more complex networks. In this chapter, we will explore some of the community detection approaches state-of-the-art research uses to deal with the increasing complexity of social networks, and particularly representing those networks as multi-mode networks (or heterogeneous networks). This chapter will cover the approaches used as well as the graph representations of complex networks. Though the work studied uses social networks as the basis for analysis, the use of multi-mode networks and hyperedges is principled in any analysis task where the complexity of the data calls for multiple types of entities with interactions involving two or more entities in the network.
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Recall from Sect. 3.2, that a metagraph is a multi-mode graph by another name.
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Acknowledgments
We would like the thank Dr. Papadopoulos and the other editors and reviewers for their helpful comments about the content of this chapter, as well as the members of the DMML lab at ASU for the same.
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Jones, I., Tang, L., Liu, H. (2015). Community Discovery in Multi-Mode Networks. In: Paliouras, G., Papadopoulos, S., Vogiatzis, D., Kompatsiaris, Y. (eds) User Community Discovery. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-23835-7_3
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