Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Advances in Graph-Based Data Mining
share announcement

Advances in Graph-Based Data Mining

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 1847

Special Issue Editor

Dr. Kamal Berahmand

E-Mail Website
Guest Editor
School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Australia
Interests: complex network; machine learning; deep learning; graph clustering

Special Issue Information

Dear Colleagues,

The Special Issue on "Advances in Graph-Based Data Mining" serves as a platform to explore the forefront of advancements and applications in graph-based techniques within the domain of data mining. It serves a dual purpose: First, it aims to showcase pioneering research that harnesses the intrinsic power of graph structures to derive invaluable insights, uncover patterns, and cultivate knowledge from intricate and interconnected datasets. Second, it aims to foster multidisciplinary collaboration by converging the domains of data mining and graph analytics. By embracing a holistic approach, this Special Issue endeavors to propel our comprehension of intricate data relationships and pave the way for groundbreaking data-driven solutions.

Dr. Kamal Berahmand
Guest Editor

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 2400 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

  • graph-based data mining
  • network analysis
  • graph algorithms
  • graph neural networks
  • community detection
  • complex data relationships
  • knowledge extraction

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

30 pages, 16108 KiB  
Article
Graphical Representation of UWF-ZeekData22 Using Memgraph
by Sikha S. Bagui, Dustin Mink, Subhash C. Bagui, Dae Hyun Sung and Farooq Mahmud
Electronics 2024, 13(6), 1015; https://doi.org/10.3390/electronics13061015 - 7 Mar 2024
Viewed by 731
Abstract
This work uses Memgraph, an open-source graph data platform, to analyze, visualize, and apply graph machine learning techniques to detect cybersecurity attack tactics in a newly created Zeek Conn log dataset, UWF-ZeekData22, generated in The University of West Florida’s cyber simulation environment. The [...] Read more.
This work uses Memgraph, an open-source graph data platform, to analyze, visualize, and apply graph machine learning techniques to detect cybersecurity attack tactics in a newly created Zeek Conn log dataset, UWF-ZeekData22, generated in The University of West Florida’s cyber simulation environment. The dataset is transformed to a representative graph, and the graph’s properties studied in this paper are PageRank, degree, bridge, weakly connected components, node and edge cardinality, and path length. Node classification is used to predict the connection between IP addresses and ports as a form of attack tactic or non-attack tactic in the MITRE framework, implemented using Memgraph’s graph neural networks. Multi-classification is performed using the attack tactics, and three different graph neural network models are compared. Using only three graph features, in-degree, out-degree, and PageRank, Memgraph’s GATJK model performs the best, with source node classification accuracy of 98.51% and destination node classification accuracy of 97.85%. Full article
(This article belongs to the Special Issue Advances in Graph-Based Data Mining)
►▼ Show Figures

Figure 1

25 pages, 4418 KiB  
Article
Top-k Graph Similarity Search Algorithm Based on Chi-Square Statistics in Probabilistic Graphs
by Ziyang Chen, Junhao Zhuang, Xuan Wang, Xian Tang, Kun Yang, Ming Du and Junfeng Zhou
Electronics 2024, 13(1), 192; https://doi.org/10.3390/electronics13010192 - 1 Jan 2024
Viewed by 631
Abstract
Top-k graph similarity search on probabilistic graphs is widely used in various scenarios, such as symptom–disease diagnostics, community discovery, visual pattern recognition, and communication networks. The state-of-the-art method uses the chi-square statistics to speed up the process. The effectiveness of the chi-square statistics [...] Read more.
Top-k graph similarity search on probabilistic graphs is widely used in various scenarios, such as symptom–disease diagnostics, community discovery, visual pattern recognition, and communication networks. The state-of-the-art method uses the chi-square statistics to speed up the process. The effectiveness of the chi-square statistics solution depends on the effectiveness of the sample observation and expectation. The existing method assumes that the labels in the data graphs are subject to uniform distribution and calculate the chi-square value based on this. In fact, however, the actual distribution of the labels does not meet the requirement of uniform distribution, resulting in a low quality of the returned results. To solve this problem, we propose a top-k similar subgraph search algorithm ChiSSA based on chi-square statistics. We propose two ways to calculate the expectation vector according to the actual distribution of labels in the graph, including the local expectation calculation method based on the vertex neighbors and the global expectation calculation method based on the label distribution of the whole graph. Furthermore, we propose two optimization strategies to improve the accuracy of query results and the efficiency of our algorithm. We conduct rich experiments on real datasets. The experimental results on real datasets show that our algorithm improves the quality and accuracy by an average of 1.66× and 1.68× in terms of time overhead, it improves by an average of 3.41×. Full article
(This article belongs to the Special Issue Advances in Graph-Based Data Mining)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop