Special Issue "Cohesive Subgraph Computation over Massive Sparse Networks"
Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 3703
Interests: graph computation; graph database; spatiotemporal; network science; data mining; 3D modelling; 3D reconstruction
Interests: 3D indoor modelling; 3D GIS; integration of BIM and GIS; 3D spatial analysis; DBMS; emergency response
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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 From this perspective, this Special Issue welcomes high-quality and unpublished papers that present significant advances in the development and application of graph model, graph computation, subgraph mining, community search/detection,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
Manuscript Submission Information
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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 2300 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.
- community search
- community detection
- pattern matching
- structural diversity search
- independent set/vertex cover
- graph coloring