Special Issue "Cohesive Subgraph Computation over Massive Sparse Networks"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Prof. Dr. Wei Li
E-Mail Website
Guest Editor
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Interests: graph computation; graph database; spatiotemporal; network science; data mining; 3D modelling; 3D reconstruction
Prof. Dr. Sisi Zlatanova
E-Mail Website
Guest Editor
School of built Environment, Faculty of Arts, Design and Architecture, University of New South Wales, Sydney, NSW 2052, Australia
Interests: geospatial information systems; data structures; database management; building information modelling; digital twin; photogrammetry and remote sensing; surveying; conceptual modelling; mobile technologies
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Special Issue Information

Dear Colleagues,

Due to the strong expressive power of the graph model, many real-world applications model data and relationships among data as graphs. With the proliferation of graph applications, such as social networks, information networks, web search, collaboration networks, E-commerce networks, communication networks, and biology, significant research efforts have been devoted towards efficiently and effectively managing and analyzing graph data. Among them, mining and querying cohesive subgraph structure in massive networks is of great importance for a deeper understanding and better management of such networks. Essentially, a cohesive subgraph is a group of vertices that are densely connected internally. For example, in the Facebook network, users with strong friendships comprise a cohesive subgraph/community; on the DBLP network, cohesive subgraphs contain researchers which share similar research interests. Owing to the importance of cohesive subgraphs, how to effectively and efficiently find communities from large graphs is an important research topic in the era of big data. In this Special Issue, we discuss the challenges and solutions of cohesive subgraph computation over large-scale graphs.

Our concrete intention in this Special Issue is to bring together researchers, scholars, and contributors to share their ongoing and latest research with regards to existing theoretical, methodological contributions as well as the development of new methods/approaches in cohesive subgraph computation over large graphs. From this perspective, this Special Issue welcomes high-quality and unpublished papers that present significant advances in the development and application of graph model, graph computation, subgraph mining, community search/detection, graph clustering, subgraph matching, and graph analysis.

Prof. Dr. Wei Li
Prof. Dr. Sisi Zlatanova
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 papers will be 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2000 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

  • community search
  • community detection
  • pattern matching
  • core/clique/plexes
  • structural diversity search
  • independent set/vertex cover
  • graph coloring

Published Papers (1 paper)

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Research

Article
Cohesive Subgraph Identification in Weighted Bipartite Graphs
Appl. Sci. 2021, 11(19), 9051; https://0-doi-org.brum.beds.ac.uk/10.3390/app11199051 - 28 Sep 2021
Viewed by 261
Abstract
Cohesive subgraph identification is a fundamental problem in bipartite graph analysis. In real applications, to better represent the co-relationship between entities, edges are usually associated with weights or frequencies, which are neglected by most existing research. To fill the gap, we propose a [...] Read more.
Cohesive subgraph identification is a fundamental problem in bipartite graph analysis. In real applications, to better represent the co-relationship between entities, edges are usually associated with weights or frequencies, which are neglected by most existing research. To fill the gap, we propose a new cohesive subgraph model, (k,ω)-core, by considering both subgraph cohesiveness and frequency for weighted bipartite graphs. Specifically, (k,ω)-core requires each node on the left layer to have at least k neighbors (cohesiveness) and each node on the right layer to have a weight of at least ω (frequency). In real scenarios, different users may have different parameter requirements. To handle massive graphs and queries, index-based strategies are developed. In addition, effective optimization techniques are proposed to improve the index construction phase. Compared with the baseline, extensive experiments on six datasets validate the superiority of our proposed methods. Full article
(This article belongs to the Special Issue Cohesive Subgraph Computation over Massive Sparse Networks)
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