Special Issue "Big Data Analysis Based Network"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 31 January 2023 | Viewed by 949

Special Issue Editors

Prof. Dr. Xiangjie Kong
E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: urban computing; mobile computing; network science

Special Issue Information

Dear Colleagues,

Entities and their interactive relations are complex and diverse in various fields, and they are not only huge in number but also rich in variance, which form a variety of networks, such as electrical power networks, transportation networks, communication networks, social networks, and biological networks. Thus, the research on networks is more challenging. With the rise of big data analysis technology, advances in the perception and analysis of various networks have been more prosperous. For example, big data analysis can improve the transmission, distribution, and control of power in the electrical power networks with the increasing requirement for greater reliability, efficiency, security, and sustainability of power, which realize smart management and maintenance.

The main aim of this Special Issue is to seek high-quality submissions that highlight big data analysis and applications in networks, address recent breakthroughs in network behaviors, network embedding learning, large-scale networks, heterogeneous networks, network visualization and storage, network anomaly analysis, etc. The topics of interest include, but are not limited to, the following:

  • Big data analysis in complex networks;
  • Network behaviors mining with big data analysis;
  • Network embedding learning technology with big data analysis;
  • Large-scale or heterogeneous network applications;
  • Dynamic spatio-temporal network analysis with big data;
  • Network visualization system with big data analysis;
  • Network structure storage with big data;
  • Network anomaly analysis and detection with big data;
  • Big data applications in electrical power networks, transportation networks, communication networks, social networks, and biological networks, etc.

Prof. Dr. Xiangjie Kong
Prof. Dr. Mario Collotta
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. Electronics 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.

Published Papers (1 paper)

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Research

Article
Gravity-Law Based Critical Bots Identification in Large-Scale Heterogeneous Bot Infection Network
Electronics 2022, 11(11), 1771; https://doi.org/10.3390/electronics11111771 - 02 Jun 2022
Viewed by 328
Abstract
The explosive growth of botnets has posed an unprecedented potent threat to the internet. It calls for more efficient ways to screen influential bots, and thus precisely bring the whole botnet down beforehand. In this paper, we propose a gravity-based critical bots identification [...] Read more.
The explosive growth of botnets has posed an unprecedented potent threat to the internet. It calls for more efficient ways to screen influential bots, and thus precisely bring the whole botnet down beforehand. In this paper, we propose a gravity-based critical bots identification scheme to assess the influence of bots in a large-scale botnet infection. Specifically, we first model the propagation of the botnet as a Heterogeneous Bot Infection Network (HBIN). An improved SEIR model is embedded into HBIN to extract both heterogeneous spatial and temporal dependencies. Within built-up HBIN, we elaborate a gravity-based influential bots identification algorithm where intrinsic influence and infection diffusion influence are specifically designed to disclose significant bots traits. Experimental results based on large-scale sample collections from the implemented prototype system demonstrate the promising performance of our scheme, comparing it with other state-of-the-art baselines. Full article
(This article belongs to the Special Issue Big Data Analysis Based Network)
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