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Network classification in temporal networks using motifs

Tu, Kun, Li, Jian, Towsley, Don, Braines, Dave and Turner, Liam 2018. Network classification in temporal networks using motifs. Presented at: 3rd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data (AALTD'18), Dublin, Ireland, 10-14 September 2018.

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Abstract

Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accuracy by up 10% compared to the state-of-the-art embedding method in network classification, for tasks such as classifying network type, identifying communities in email exchange network, and identifying users given their app-switching behaviors.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Related URLs:
Date of First Compliant Deposit: 14 August 2018
Last Modified: 14 Aug 2018 16:46
URI: http://orca.cf.ac.uk/id/eprint/114018

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