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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: 15 January 2025 | Viewed by 4294

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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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13 pages, 9395 KiB  
Article
Sex Differences in Hierarchical and Modular Organization of Functional Brain Networks: Insights from Hierarchical Entropy and Modularity Analysis
by Wenyu Chen, Ling Zhan and Tao Jia
Entropy 2024, 26(10), 864; https://doi.org/10.3390/e26100864 - 14 Oct 2024
Viewed by 812
Abstract
Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not [...] Read more.
Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not been fully investigated. Here, we explore whether the hierarchical structure of the brain network differs between females and males using resting-state fMRI data. We measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. At the mean level, females show higher modularity, whereas males exhibit a more complex hierarchy. At the consensus level, we use a co-classification matrix to perform a detailed investigation of the differences in the hierarchical organization between sexes and observe that the female group and the male group exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our findings suggest that the brains of females and males employ different network topologies to carry out brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in the hierarchical organization of the brain network, which sheds light on future studies of brain functions. Full article
(This article belongs to the Special Issue Community Detection and Clustering Complex Networks and Their Applications)
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18 pages, 4135 KiB  
Article
Effective Temporal Graph Learning via Personalized PageRank
by Ziyu Liao, Tao Liu, Yue He and Longlong Lin
Entropy 2024, 26(7), 588; https://doi.org/10.3390/e26070588 - 10 Jul 2024
Viewed by 943
Abstract
Graph representation learning aims to map nodes or edges within a graph using low-dimensional vectors, while preserving as much topological information as possible. During past decades, numerous algorithms for graph representation learning have emerged. Among them, proximity matrix representation methods have been shown [...] Read more.
Graph representation learning aims to map nodes or edges within a graph using low-dimensional vectors, while preserving as much topological information as possible. During past decades, numerous algorithms for graph representation learning have emerged. Among them, proximity matrix representation methods have been shown to exhibit excellent performance in experiments and scale to large graphs with millions of nodes. However, with the rapid development of the Internet, information interactions are happening at the scale of billions every moment. Most methods for similarity matrix factorization still focus on static graphs, leading to incomplete similarity descriptions and low embedding quality. To enhance the embedding quality of temporal graph learning, we propose a temporal graph representation learning model based on the matrix factorization of Time-constrained Personalize PageRank (TPPR) matrices. TPPR, an extension of personalized PageRank (PPR) that incorporates temporal information, better captures node similarities in temporal graphs. Based on this, we use Single Value Decomposition or Nonnegative Matrix Factorization to decompose TPPR matrices to obtain embedding vectors for each node. Through experiments on tasks such as link prediction, node classification, and node clustering across multiple temporal graphs, as well as a comparison with various experimental methods, we find that graph representation learning algorithms based on TPPR matrix factorization achieve overall outstanding scores on multiple temporal datasets, highlighting their effectiveness. Full article
(This article belongs to the Special Issue Community Detection and Clustering Complex Networks and Their Applications)
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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 - 3 Dec 2023
Viewed by 1902
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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