Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.1
2.8
Journals
Future Internet
Special Issues
Graph Machine Learning and Complex Networks
share announcement

Graph Machine Learning and Complex Networks

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 5430

Special Issue Editors

Prof. Dr. Vincenza Carchiolo

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica Elettronica Informatica, Università di Catania, Viale Andrea Doria, 9-95127 Catania, Italy
Interests: network science; natural language processing; data analysis; machine learning; information spread; distributed systems
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Grassia

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica, Elettronica Informatica (DIEEI), Università di Catania, I95125 Catania, Italy
Interests: geometric deep learning; machine learning; network science; social network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, two major research challenges are machine/deep learning and complex networks and both have inter-disciplinary characteristics. Graph machine learning is a novel branch of machine learning that deals with graph-based data to design from expertise, whereas complex networks permit the modeling of large systems through a graph exploiting its formal nature.

Machine and deep learning both assist with a wide range of problems in different areas whilst complex networks are able to model a lot of practical settings, including engineering, neuroscience, social networks, geoscience, economics, etc.

Since complex networks and graph machine learning are closely related, this Special Issue focus on method, strategies, and techniques based on graph machine learning applied to networks to leverage the performance of graph machine learning techniques with high efficiency.

Prof. Dr. Vincenza Carchiolo
Dr. Marco Grassia
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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

  • deep generative models for graphs
  • geometric deep learning
  • graph neural networks
  • graph structure of the web
  • knowledge graphs
  • node embeddings and classification
  • application

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

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

28 pages, 6446 KiB  
Article
A Graph DB-Based Solution for Semantic Technologies in the Future Internet
by Stefano Ferilli, Eleonora Bernasconi, Davide Di Pierro and Domenico Redavid
Future Internet 2023, 15(10), 345; https://doi.org/10.3390/fi15100345 - 20 Oct 2023
Cited by 4 | Viewed by 2219
Abstract
With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, the level of knowledge, not just information, must [...] Read more.
With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, the level of knowledge, not just information, must be handled by systems where explicit relationships among objects are represented and processed. For this purpose, the knowledge representation branch of AI proposes Knowledge Graphs, widely used in the Semantic Web, where different online applications may interact by understanding the meaning of the data they process and exchange. This paper describes a framework and online platform for the Internet-based knowledge graph definition, population, and exploitation based on the LPG graph model. Its main advantages are its efficiency and representational power and the wide range of functions that it provides to its users beyond traditional Semantic Web reasoning: network analysis, data mining, multistrategy reasoning, and knowledge browsing. Still, it can also be mapped onto the SW. Full article
(This article belongs to the Special Issue Graph Machine Learning and Complex Networks)
►▼ Show Figures

Figure 1

18 pages, 11580 KiB  
Article
Using a Graph Engine to Visualize the Reconnaissance Tactic of the MITRE ATT&CK Framework from UWF-ZeekData22
by Sikha S. Bagui, Dustin Mink, Subhash C. Bagui, Michael Plain, Jadarius Hill and Marshall Elam
Future Internet 2023, 15(7), 236; https://doi.org/10.3390/fi15070236 - 6 Jul 2023
Viewed by 2603
Abstract
There has been a great deal of research in the area of using graph engines and graph databases to model network traffic and network attacks, but the novelty of this research lies in visually or graphically representing the Reconnaissance Tactic (TA0043) of the [...] Read more.
There has been a great deal of research in the area of using graph engines and graph databases to model network traffic and network attacks, but the novelty of this research lies in visually or graphically representing the Reconnaissance Tactic (TA0043) of the MITRE ATT&CK framework. Using the newly created dataset, UWF-Zeekdata22, based on the MITRE ATT&CK framework, patterns involving network connectivity, connection duration, and data volume were found and loaded into a graph environment. Patterns were also found in the graphed data that matched the Reconnaissance as well as other tactics captured by UWF-Zeekdata22. The star motif was particularly useful in mapping the Reconnaissance Tactic. The results of this paper show that graph databases/graph engines can be essential tools for understanding network traffic and trying to detect network intrusions before they happen. Finally, an analysis of the runtime performance of the reduced dataset used to create the graph databases showed that the reduced datasets performed better than the full dataset. Full article
(This article belongs to the Special Issue Graph Machine Learning and Complex Networks)
►▼ 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