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Applied Sciences
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Cohesive Subgraph Computation over Massive Sparse Networks
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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: closed (20 September 2022) | Viewed by 5748

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
Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
Interests: 3D indoor modelling; 3D GIS; integration of BIM and GIS; 3D spatial analysis; DBMS; emergency response
Special Issues, Collections and Topics in MDPI journals

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

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Keywords

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

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Published Papers (2 papers)

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Research

16 pages, 4857 KiB  
Article
MreNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama
by Bing Xu, Yaohui Sun, Xiangxu Meng, Zhihan Liu and Wei Li
Appl. Sci. 2022, 12(19), 9696; https://doi.org/10.3390/app12199696 - 27 Sep 2022
Cited by 3 | Viewed by 1781
Abstract
The major problem with 3D room layout reconstruction is estimating the 3D room layout from a single panoramic image. In practice, the boundaries between indoor objects are difficult to define, for example, the boundary position of a sofa and a table, and the [...] Read more.
The major problem with 3D room layout reconstruction is estimating the 3D room layout from a single panoramic image. In practice, the boundaries between indoor objects are difficult to define, for example, the boundary position of a sofa and a table, and the boundary position of a picture frame and a wall. We propose MreNet, a novel neural network architecture for predicting 3D room layout, which outperforms previous state-of-the-art approaches. It can efficiently model the overall layout of indoor rooms through a global receptive field and sparse attention mechanism, while prior works tended to use CNNs to gradually increase the receptive field. Furthermore, the proposed feature connection mechanism can solve the problem of the gradient disappearing in the process of training, and feature maps of different granularity can be obtained in different layers. Experiments on both cuboid-shaped and general Manhattan layouts show that the proposed work outperforms recent algorithms in prediction accuracy. Full article
(This article belongs to the Special Issue Cohesive Subgraph Computation over Massive Sparse Networks)
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15 pages, 3268 KiB  
Article
Cohesive Subgraph Identification in Weighted Bipartite Graphs
by Xijuan Liu and Xiaoyang Wang
Appl. Sci. 2021, 11(19), 9051; https://doi.org/10.3390/app11199051 - 28 Sep 2021
Cited by 2 | Viewed by 2078
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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