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

Journal Article · · ACM Transactions on Knowledge Discovery from Data
DOI:https://doi.org/10.1145/3451527· OSTI ID:1810757
 [1];  [2];  [1]
  1. Syracuse Univ., NY (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
+ Show Author Affiliations

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.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1810757
Report Number(s):
SAND-2020-14338J; 693137
Journal Information:
ACM Transactions on Knowledge Discovery from Data, Vol. 16, Issue 1; ISSN 1556-4681
Publisher:
Association for Computing Machinery (ACM)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

97 MATHEMATICS AND COMPUTING
96 KNOWLEDGE MANAGEMENT AND PRESERVATION
graph algorithms
information systems
data mining
world wide web
social networks