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    The Impact of Interaction and Algorithm Choice on Identified Communities

    Maher, Rana and Malone, David and Wallace, Marie (2017) The Impact of Interaction and Algorithm Choice on Identified Communities. In: 2017 International Conference On Social Media, Wearable And Web Analytics (Social Media), 19-20 June 2017, London.

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    In social networks, nodes are organized into densely linked communities where edges appear among the nodes with high concentration. Identifying communities has proven to be a challenging task due to various community definitions/algorithms and also due to the lack of “ground truth” for reference and evaluation. These communities not only differ due to various definitions but also can be affected by the type of interactions modeled in the network, which lead to different social groups. We are interested in exploring and studying the concept of partial network views, which is based on multiple types of interactions. An Enron email network is used to conduct our experiments. In this paper, we explore the mutual impact of selecting different views extracted from the same network and their interplay with various community detection algorithms to measure the change and the level of realism of the structure for non-overlapping communities. To better understand this, we assess the agreement of partitions by evaluating the partitioning quality (performance) and finding the similarity between algorithms. The results demonstrate that the topological properties of communities and the performance of algorithms are equivalent to each other. Both of them are affected by the type of interaction specified in each view. Some network views appeared to have more interesting communities than other views, thus, might help to approach a relatively informative and logic “ground truth” for communities.

    Item Type: Conference or Workshop Item (Paper)
    Additional Information: This is the preprint version of the published paper, which is available at DOI: 10.1109/SOCIALMEDIA.2017.8057361
    Keywords: Community detection; Community structure; Similarity measures; Partitions comparison;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 10060
    Depositing User: Dr. David Malone
    Date Deposited: 04 Oct 2018 14:32
    Refereed: Yes
    Funders: Science Foundation Ireland (SFI), European Regional Development Fund
      Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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