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 2616
Special Issue Editor
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.
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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