Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification
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Existing approaches for detecting anomalous events in time-evolving networks usually focus on detecting events involving the majority of the nodes, which affect the overall structure of the network. Since events involving just a small subset of nodes usually do not affect the overall structure of the network, they are more difficult to spot. In this context, tensor decomposition based methods usually beat other techniques in detecting global events, but fail at spotting localized event patterns. We tackle this problem by replacing the batch decomposition with a sliding window decomposition, which is further mined in an unsupervised way using statistical tools. Via experimental results in one synthetic and four real-world networks, we show the potential of the proposed method in the detection and specification of local events.
KeywordsTime-evolving social networks Tensor decomposition Event detection
This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019. Sofia Fernandes also acknowledges the support of FCT via the PhD grant PD/BD/114189/2016. The authors also acknowledge the SocioPatterns collaboration for making the dataset (in ) available.
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