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Title: MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks

Abstract

In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline—the similarity of the sample that MCS+ generates to the real network is up to three times that of the bestmore » baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.« less

Authors:
 [1];  [2];  [1]
  1. Syracuse Univ., NY (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1810757
Report Number(s):
SAND-2020-14338J
Journal ID: ISSN 1556-4681; 693137
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
ACM Transactions on Knowledge Discovery from Data
Additional Journal Information:
Journal Volume: 16; Journal Issue: 1; Journal ID: ISSN 1556-4681
Publisher:
Association for Computing Machinery (ACM)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 96 KNOWLEDGE MANAGEMENT AND PRESERVATION; graph algorithms; information systems; data mining; world wide web; social networks

Citation Formats

Laishram, Ricky, Wendt, Jeremy D., and Soundarajan, Sucheta. MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks. United States: N. p., 2021. Web. doi:10.1145/3451527.
Laishram, Ricky, Wendt, Jeremy D., & Soundarajan, Sucheta. MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks. United States. https://doi.org/10.1145/3451527
Laishram, Ricky, Wendt, Jeremy D., and Soundarajan, Sucheta. 2021. "MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks". United States. https://doi.org/10.1145/3451527. https://www.osti.gov/servlets/purl/1810757.
@article{osti_1810757,
title = {MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks},
author = {Laishram, Ricky and Wendt, Jeremy D. and Soundarajan, Sucheta},
abstractNote = {In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline—the similarity of the sample that MCS+ generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.},
doi = {10.1145/3451527},
url = {https://www.osti.gov/biblio/1810757}, journal = {ACM Transactions on Knowledge Discovery from Data},
issn = {1556-4681},
number = 1,
volume = 16,
place = {United States},
year = {2021},
month = {7}
}

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