Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Silva Fernandes, Sd (Author) Other; T, HF (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: Discovery Science - 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019, Proceedings

Pages: 385-395

22nd International Conference on Discovery Science, DS 2019

28 October 2019 through 30 October 2019

Indexing

Scopus - 4 Citations

Other information

Authenticus ID: P-00R-FF4

DOI: 10.1007/978-3-030-33778-0_29

Abstract (EN): 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. © Springer Nature Switzerland AG 2019.

Language: English

Type (Professor's evaluation): Scientific