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Selecting Informative Features for Post-hoc Community Explanation

Lookup NU author(s): Dr Daniel ArchambaultORCiD


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© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Community finding algorithms are complex, often stochastic algorithms used to detect highly-connected groups of nodes in a graph. As with “black-box” machine learning models, these algorithms typically provide little in the way of explanation or insight into their outputs. In this research paper, inspired by recent work in explainable artificial intelligence (XAI), we look to develop post-hoc explanations for community finding, which are agnostic of the choice of algorithm. Specifically, we propose a new approach to identify features that indicate whether a set of nodes comprises a coherent community or not. We evaluate our methodology, which selects interpretable features from a longlist of candidates, in the context of three well-known community finding algorithms.

Publication metadata

Author(s): Sadler S, Greene D, Archambault D

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Complex Networks and Their Applications X

Year of Conference: 2021

Pages: 297-308

Print publication date: 01/01/2022

Online publication date: 01/01/2022

Acceptance date: 02/04/2018

Publisher: Springer


DOI: 10.1007/978-3-030-93409-5_25

Library holdings: Search Newcastle University Library for this item

Series Title: Studies in Computational Intelligence

ISBN: 9783030934088