Mining Dense Subgraphs with Similar Edges

Polina Rozenshtein*, Giulia Preti, Aristides Gionis, Yannis Velegrakis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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Abstract

When searching for interesting structures in graphs, it is often important to take into account not only the graph connectivity, but also the metadata available, such as node and edge labels, or temporal information. In this paper we are interested in settings where such metadata is used to define a similarity between edges. We consider the problem of finding subgraphs that are dense and whose edges are similar to each other with respect to a given similarity function. Depending on the application, this function can be, for example, the Jaccard similarity between the edge label sets, or the temporal correlation of the edge occurrences in a temporal graph. We formulate a Lagrangian relaxation-based optimization problem to search for dense subgraphs with high pairwise edge similarity. We design a novel algorithm to solve the problem through parametric min-cut [15, 17], and provide an efficient search scheme to iterate through the values of the Lagrangian multipliers. Our study is complemented by an evaluation on real-world datasets, which demonstrates the usefulness and efficiency of the proposed approach.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III
EditorsFrank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
Place of PublicationCham
PublisherSpringer
Pages20-36
Number of pages17
Edition1
ISBN (Electronic)978-3-030-67664-3
ISBN (Print)978-3-030-67663-6
DOIs
Publication statusPublished - 25 Feb 2021
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: 14 Sept 202018 Sept 2020

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12459
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online
Period14/09/2018/09/20

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