Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries

Charalampos E. Tsourakakis, Tianyi Chen, Naonori Kakimura, Jakub Pachocki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the densest subgraph problem, given an undirected graph G(V, E, w) with non-negative edge weights we are asked to find a set of nodes S⊆V that maximizes the degree density w(S)/|S|, where w(S) is the sum of the weights of the edges in the graph induced by S. This problem is solvable in polynomial time, and in general is well studied. But what happens when the edge weights are negative? Is the problem still solvable in polynomial time? Also, why should we care about the densest subgraph problem in the presence of negative weights? In this work we answer the aforementioned questions. Specifically, we provide two novel graph mining primitives that are applicable to a wide variety of applications. Our primitives can be used to answer questions such as “how can we find a dense subgraph in Twitter with lots of replies and mentions but no follows?”, “how do we extract a dense subgraph with high expected reward and low risk from an uncertain graph”? We formulate both problems mathematically as special instances of dense subgraph discovery in graphs with negative weights. We study the hardness of the problem, and we prove that the problem in general is NP-hard, but we also provide sufficient conditions under which it is poly-time solvable. We design an efficient approximation algorithm that works best in the presence of small negative weights, and an effective heuristic for the more general case. Finally, we perform experiments on various real-world datasets that verify the value of the proposed primitives, and the effectiveness of our proposed algorithms. The code and the data are available at https://github.com/nega-tivedsd.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
EditorsUlf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
PublisherSpringer
Pages378-394
Number of pages17
ISBN (Print)9783030461492
DOIs
Publication statusPublished - 2020
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany
Duration: 2019 Sep 162019 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11906 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
CountryGermany
CityWurzburg
Period19/9/1619/9/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tsourakakis, C. E., Chen, T., Kakimura, N., & Pachocki, J. (2020). Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings (pp. 378-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11906 LNAI). Springer. https://doi.org/10.1007/978-3-030-46150-8_23