research-article

When Less Is More: Systematic Analysis of Cascade-Based Community Detection

Authors:

Liudmila Prokhorenkova,

Alexey Tikhonov,

Nelly LitvakAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 4

Article No.: 78, Pages 1 - 22

https://doi.org/10.1145/3494563

Published: 08 January 2022 Publication History

Abstract

Information diffusion, spreading of infectious diseases, and spreading of rumors are fundamental processes occurring in real-life networks. In many practical cases, one can observe when nodes become infected, but the underlying network, over which a contagion or information propagates, is hidden. Inferring properties of the underlying network is important since these properties can be used for constraining infections, forecasting, viral marketing, and so on. Moreover, for many applications, it is sufficient to recover only coarse high-level properties of this network rather than all its edges. This article conducts a systematic and extensive analysis of the following problem: Given only the infection times, find communities of highly interconnected nodes. This task significantly differs from the well-studied community detection problem since we do not observe a graph to be clustered. We carry out a thorough comparison between existing and new approaches on several large datasets and cover methodological challenges specific to this problem. One of the main conclusions is that the most stable performance and the most significant improvement on the current state-of-the-art are achieved by our proposed simple heuristic approaches agnostic to a particular graph structure and epidemic model. We also show that some well-known community detection algorithms can be enhanced by including edge weights based on the cascade data.

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Cited By

Zhang GZhang SYuan G(2024)Bayesian Graph Local Extrema Convolution with Long-tail Strategy for Misinformation DetectionACM Transactions on Knowledge Discovery from Data10.1145/363940818:4(1-21)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3639408
Emadi MRahgozar MOroumchian F(2023)Inferring Diffusion Network from Information Cascades using Transitive InfluenceJournal of Information Systems and Telecommunication (JIST)10.61186/jist.33656.11.44.30711:44(307-319)Online publication date: 16-Dec-2023
https://doi.org/10.61186/jist.33656.11.44.307
Xing YJohansson K(2023)Almost Exact Recovery in Gossip Opinion Dynamics Over Stochastic Block Models2023 62nd IEEE Conference on Decision and Control (CDC)10.1109/CDC49753.2023.10383465(2421-2426)Online publication date: 13-Dec-2023
https://doi.org/10.1109/CDC49753.2023.10383465
Show More Cited By

Index Terms

When Less Is More: Systematic Analysis of Cascade-Based Community Detection
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
  2. Randomness, geometry and discrete structures
    1. Random network models

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Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 4

August 2022

529 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3505210

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 08 January 2022

Accepted: 01 October 2021

Revised: 01 October 2021

Received: 01 May 2021

Published in TKDD Volume 16, Issue 4

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Ministry of Education and Science of the Russian Federation in the framework of MegaGrant
Russian President

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Cited By

Zhang GZhang SYuan G(2024)Bayesian Graph Local Extrema Convolution with Long-tail Strategy for Misinformation DetectionACM Transactions on Knowledge Discovery from Data10.1145/363940818:4(1-21)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3639408
Emadi MRahgozar MOroumchian F(2023)Inferring Diffusion Network from Information Cascades using Transitive InfluenceJournal of Information Systems and Telecommunication (JIST)10.61186/jist.33656.11.44.30711:44(307-319)Online publication date: 16-Dec-2023
https://doi.org/10.61186/jist.33656.11.44.307
Xing YJohansson K(2023)Almost Exact Recovery in Gossip Opinion Dynamics Over Stochastic Block Models2023 62nd IEEE Conference on Decision and Control (CDC)10.1109/CDC49753.2023.10383465(2421-2426)Online publication date: 13-Dec-2023
https://doi.org/10.1109/CDC49753.2023.10383465
Xing YHe XFang HJohansson K(2023)Community structure recovery and interaction probability estimation for gossip opinion dynamicsAutomatica (Journal of IFAC)10.1016/j.automatica.2023.111105154:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.automatica.2023.111105

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