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Entropy
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Community Detection and Clustering Complex Networks and Their Applications
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Community Detection and Clustering Complex Networks and Their Applications

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1468

Special Issue Editors

Prof. Dr. Boleslaw K. Szymanski

E-Mail Website
Guest Editor
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
Interests: bionetworks; covert networks; political networks; social network evolution; dynamics of complex networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kevin E. Bassler

E-Mail Website
Guest Editor
Department of Physics, University of Houston, Houston, TX 77004, USA
Interests: dynamics of complex networks; adaptive or co-evolving networks; neural networks; bionetworks; random networks
Prof. Dr. Tao Jia

E-Mail Website
Guest Editor
Director of the Office of Information and Technology, Southwest University, Chongqing 400715, China
Interests: network embedding; diffusion processes on networks; scholar/citation network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few years to characterize, model, and analyze communities, more investigations are needed to better understand the impact of their structure and dynamics on networked systems. Therefore, the primary goal of this Special Issue is to demonstrate the cutting-edge research advances in community structures in networks in order to provide a landscape of research progress and application potentials in related areas.

Papers ranging from a broad nature to focusing on various aspects of community structure with strong algorithmic innovations and also application-oriented works are solicited.

Topics include, but are not limited to, the following:

  • Models of communities;
  • Embedding models of communities;
  • Evolution/temporal communities;
  • Dynamics of communities;
  • Community detection;
  • Communities in uncertain data;
  • Entropy metrics for communities;
  • Visual representation of communities;
  • Parallel algorithms for communities;
  • Hierarchy and ego-networks;
  • Communities and sampling;
  • Communities and controllability;
  • Communities and synchronization;
  • Communities and machine learning;
  • Communities and resilience;
  • Communities and link prediction;
  • Communities in social networks;
  • Communities in multiplex;
  • Communities in economics and finance;
  • Communities in epidemics;
  • Communities in rumor spreading;
  • Communities in mobile networks;
  • Communities in biological networks;
  • Communities in the brain;
  • Communities in technological networks.

Prof. Dr. Boleslaw K. Szymanski
Prof. Dr. Kevin E. Bassler
Prof. Dr. Tao Jia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dynamics of complex networks
  • information spreading
  • heterogeneous information networks
  • adaptive networks
  • co-evolving networks
  • bionetworks
  • political networks
  • networks with ambiguous structures

Published Papers (1 paper)

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Research

27 pages, 3781 KiB  
Article
Spectral Clustering Community Detection Algorithm Based on Point-Wise Mutual Information Graph Kernel
by Yinan Chen, Wenbin Ye and Dong Li
Entropy 2023, 25(12), 1617; https://doi.org/10.3390/e25121617 - 03 Dec 2023
Viewed by 1171
Abstract
To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual information (PMI) graph kernel. The kernel is constructed according to the [...] Read more.
To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual information (PMI) graph kernel. The kernel is constructed according to the point-wise mutual information between nodes, which is then used as a proximity matrix to reconstruct the network and obtain the symmetric normalized Laplacian matrix. Finally, the network is partitioned by the eigendecomposition and eigenvector clustering of the Laplacian matrix. In addition, to determine the number of clusters during spectral clustering, this paper proposes a fast algorithm, BI-CNE, for estimating the number of communities. For a specific network, the algorithm first reconstructs the original network and then runs Monte Carlo sampling to estimate the number of communities by Bayesian inference. Experimental results show that the detection speed and accuracy of the algorithm are superior to other existing algorithms for estimating the number of communities. On this basis, the spectral clustering community detection algorithm PMIK-SC also has high accuracy and stability compared with other community detection algorithms and spectral clustering algorithms. Full article
(This article belongs to the Special Issue Community Detection and Clustering Complex Networks and Their Applications)
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