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gl2vec: Learning feature representation using graphlets for directed networks

Tu, Kun, Li, Jian, Towsley, Don, Braines, David and Turner, Liam 2019. gl2vec: Learning feature representation using graphlets for directed networks. Presented at: 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), Vancouver, BC, Canada, 27-30 August 2019. Proceedings of ASONAM 19. Association for Computing Machinery, 10.1145/3341161.3342908

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Abstract

Learning network representation has a variety of applications, such as network classification. Most existing work in this area focuses on static undirected networks and does not account for presence of directed edges or temporal changes. Furthermore, most work focuses on node representations that do poorly on tasks like network classification. In this paper, we propose a novel network embedding methodology, gl2vec, for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs. We demonstrate the efficacy and usability of gl2vec over existing state-of-the-art methods on network classification tasks such as network type classification and subgraph identification in several real-world static and temporal directed networks. We argue that gl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10% in real-world applications

Item Type: Conference or Workshop Item (Paper)
Status: In Press
Schools: Computer Science & Informatics
Crime and Security Research Institute (CSURI)
Publisher: Association for Computing Machinery
ISBN: 781450368681
Date of First Compliant Deposit: 16 October 2019
Last Modified: 30 Nov 2019 06:59
URI: http://orca.cf.ac.uk/id/eprint/126029

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