Handling Noisy Constraints in Semi-supervised Overlapping Community Finding

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dc.contributor.authorAlghamdi, Elham-
dc.contributor.authorRushe, Ellen-
dc.contributor.authorHossein Zadeh Bazargani, Mehran-
dc.contributor.authorMacNamee, Brian-
dc.contributor.authorGreene, Derek-
dc.descriptionThe 8th International Conference on Complex Networks and their Applications (Complex Networks 2019), Lisbon, Portugal, 10-12 December 2019en_US
dc.description.abstractCommunity structure is an essential property that helps us to understand the nature of complex networks. Since algorithms for detecting communities are unsupervised in nature, they can fail to uncover useful groupings, particularly when the underlying communities in a network are highly overlapping [1]. Recent work has sought to address this via semi-supervised learning [2], using a human annotator or “oracle” to provide limited supervision. This knowledge is typically encoded in the form of must-link and cannot-link constraints, which indicate that a pair of nodes should always be or should never be assigned to the same community. In this way, we can uncover communities which are otherwise difficult to identify via unsupervised techniques. However, in real semi-supervised learning applications, human supervision may be unreliable or “noisy”, relying on subjective decision making [3]. Annotators can disagree with one another, they might only have limited knowledge of a domain, or they might simply complete a labeling task incorrectly due to the burden of annotation. Thus, we might reasonably expect that the pairwise constraints used in a real semi-supervised community detection task could be imperfect or conflicting. The aim of this study is to explore the effect of noisy, incorrectly-labeled constraints on the performance of semisupervised community finding algorithms for overlapping networks. Furthermore, we propose an approach to mitigate such cases in real-world network analysis tasks. We treat noisy pairwise constraints as anomalies, and use an autoencoder, a commonlyused method in the domain of anomaly detection, to identify such constraints. Initial experiments on synthetic network demonstrate the usefulness of this approach.en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.subjectMachine Learning & Statisticsen_US
dc.subjectCommunity structureen_US
dc.subjectComplex networksen_US
dc.titleHandling Noisy Constraints in Semi-supervised Overlapping Community Findingen_US
dc.typeConference Publicationen_US
dc.statusPeer revieweden_US
dc.neeo.contributorHossein Zadeh Bazargani|Mehran|aut|-
dc.description.adminProceedings will be published by Springer - ACen_US
dc.description.adminEmbargo until published plus 12 month embargo once published - ACen_US
dc.description.adminUpdate citation details during checkdate report - ACen_US
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