Journal Information

Information Sciences

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Impact Factor:
6.8
Publisher:
Elsevier
ISSN:
0020-0255
Viewed:
118205
Tracked:
165

Call For Papers

Information Sciences is an academic journal published by Elsevier. (ISSN 0020-0255, impact factor 6.8, CCF B).

Informatics and Computer Science Intelligent Systems Applications An International Journal Information Sciences will publish original, innovative and creative research results. A smaller number of timely tutorial and surveying contributions will be published from time to time. The journal is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in information, knowledge engineering and intelligent systems. Readers are assumed to have a common interest in information science, but with diverse backgrounds in fields such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioural sciences and biochemistry. The journal publishes high-quality, refereed articles. It emphasizes a balanced coverage of both theory and practice. It fully acknowledges and vividly promotes a breadth of the discipline of Informations Sciences. Topics include: Foundations of Information Science: Information Theory, Mathematical Linguistics, Automata Theory, Cognitive Science, Theories of Qualitative Behaviour, Artificial Intelligence, Computational Intelligence, Soft Computing, Semiotics, Computational Biology and Bio-informatics. Implementations and Information Technology: Intelligent Systems, Genetic Algorithms and Modelling, Fuzzy Logic and Approximate Reasoning, Artificial Neural Networks, Expert and Decision Support Systems, Learning and Evolutionary Computing, Expert and Decision Support Systems, Learning and Evolutionary Computing, Biometrics, Moleculoid Nanocomputing, Self-adaptation and Self-organisational Systems, Data Engineering, Data Fusion, Information and Knowledge, Adaptive ad Supervisory Control, Discrete Event Systems, Symbolic / Numeric and Statistical Techniques, Perceptions and Pattern Recognition, Design of Algorithms, Software Design, Computer Systems and Architecture Evaluations and Tools, Human-Computer Interface, Computer Communication Networks and Modelling and Computing with Words Applications: Manufacturing, Automation and Mobile Robots, Virtual Reality, Image Processing and Computer Vision Systems, Photonics Networks, Genomics and Bioinformatics, Brain Mapping, Language and Search Engine Design, User-friendly Man Machine Interface, Data Compression and Text Abstraction and Summarization, Virtual Reality, Finance and Economics Modelling and Optimisation Executive Editors-in-Chief can be reached at the following: Prof. Dr. Tofigh TA Allahviranloo- tofigh.allahviranloo@istinye.edu.tr Professor Sabrina S. Senatore- ssenatore@unisa.it Professor Zheng Z. Yan- zhengyan.pz@gmail.com
Last updated by Dou Sun in

Special Issues

Special Issue on Graph-based solutions for Artificial Intelligence Submission Date: 2026-06-30 Graph-based Artificial Intelligence (AI) is a term used to describe methodologies in which graphs play a central role in representing and enhancing intelligent systems. Graphs, as mathematical structures composed of nodes and edges, naturally encode relationships, dependencies, and interactions among entities. This intrinsic property makes them not only suitable for modeling data with an explicit relational structure (e.g., molecules, social networks, or transportation systems) but also particularly effective in providing an additional layer of abstraction, organization, and reasoning in complex AI scenarios. A particular perspective of graph-based AI is one that focuses on approaches in which graphs are employed as complementary or internal representations to support and enrich AI models. These graph-augmented approaches leverage the expressive power of graphs to integrate external knowledge, enhance interpretability, and provide structural insights into the functioning of AI systems. Notable examples include the integration of knowledge graphs into Large Language Models (LLMs) to provide factual grounding and reduce hallucinations, or the use of graphs to improve explainability by explicitly representing dependencies among features, layers, or attention mechanisms. Beyond the integration of external knowledge, graphs also offer a unifying framework for the representation and analysis of the internal structure of AI models. Neural networks and deep learning models can be described as graphs that capture tasks, data flows, and interlayer relationships, including attention mechanisms. This perspective offers a potential avenue for enhancing efficiency through model compression and reduced computational costs, while concurrently promoting transparency and interpretability by making information flows and feature interactions explicit. Guest editors: Prof. Domenico Ursino (Executive Guest Editor) Polytechnic University of Marche, Ancona, Italy Email: d.ursino@univpm.it Fields of Interest: Cooperative Information Systems, Artificial Intelligence, Social Network Analysis, Complex Network Analysis, Data Engineering, Data Science, Deep Learning Prof. Luca Virgili Polytechnic University of Marche, Ancona, Italy Email: luca.virgili@univpm.it Fields of interest: Artificial Intelligence, Complex Network Analysis, Data Science, Deep Learning Prof. Alessia Amelio University of Chieti-Pescara, Chieti, Italy Email: alessia.amelio@unich.it Fields of interest: data mining, artificial intelligence, machine learning, deep learning, pattern recognition, complex networks Prof. Gianluca Bonifazi Polytechnic University of Marche, Ancona, Italy Email: g.bonifazi@univpm.it Fields of interest: Complex Network Analysis, Artificial Intelligence, Blockchain Dr. Michele Marchetti Polytechnic University of Marche, Ancona, Italy Email: michele.marchetti@staff.univpm.it Fields of interest: Social Network Analysis, Natural Language Processing, Artificial Intelligence, Vision Transformers, Deep Learning Prof. Radu Tudor Ionescu University of Bucharest, Bucureşti, Romania Email: radu.ionescu@fmi.unibuc.ro Fields of interest: Computer Vision, Machine Learning, Artificial Intelligence, Computational Linguistics, Medical Imaging Special issue information: Despite the substantial advancements in Artificial Intelligence (AI) witnessed in recent years, contemporary AI systems continue to grapple challenges pertaining to interpretability, efficiency, and reliability. Conventional deep learning models frequently function as black boxes, which complicates the interpretation of their decisions and the assurance of their reliability in practical real-world applications. Concurrently, the accelerated development of Large Language Models (LLMs) and generative AI has given rise to concerns regarding factual grounding, hallucinations, and computational costs. Graph-based approaches offer potential solutions to these limitations by encoding entities and their relationships, thereby providing an abstraction layer that can support AI models. This property can be leveraged to address the current open issues of AI models. Indeed: • As for interpretability, graphs can make dependencies transparent. For example, they can model how features, layers, or tokens influence one another. In this manner, implicit neural computations can be turned into explicit relational explanations. • As for factual grounding and reliability, knowledge graphs integrated into LLMs provide external, curated sources of truth. In this manner, hallucinations can be reduced and trustworthiness can be improved. • As for efficiency, graphs enable compact structural representations. These representations can guide model compression, pruning, and scalable inference. In this manner, both training and inference costs can be reduced. • As for robustness, graph formulations can highlight anomalies or inconsistencies in data. In this manner, more resilient and bias-aware AI systems can be supported. • As for multimodal integration, graphs act as a unifying layer. In this layer, information from text, images, and signals can be aligned into coherent structures. In this manner, richer connections across modalities can be enabled. The objective of this special issue is to provide a forum for researchers from diverse scientific communities to present their recent findings on graph-based approaches to addressing the aforementioned open problems regarding AI. In particular, it aims to collect papers that illustrate how the inherent flexibility of graph-based frameworks (including traditional graphs, multiplex networks, and multilayer networks) can be utilized to effectively address and overcome these challenges. Authors are encouraged to highlight innovative techniques and methodologies that relate graph theory and Artificial Intelligence systems, as well as to identify perspectives for future generations in this rapidly evolving field. This special issue is interested in, but not limited to, the following topics: • Graphs in the design and optimization of Artificial Intelligence models; • Graph-based approaches for explainable Artificial Intelligence; • Optimization strategies for graph-based models in AI; • Graph-augmented methods for Generative Artificial Intelligence; • Graph-based techniques to mitigate hallucinations in Generative AI systems; • Applications of graph-based Artificial Intelligence across domains; • Graph-based Artificial Intelligence for Social Network Analysis; • Graph-based Artificial Intelligence for Complex Network Analysis; • Graph-based Artificial Intelligence in educational technologies; • Graph-based Artificial Intelligence in biomedical domains; • Graph-inspired approaches for neuromorphic and brain-inspired systems. Manuscript submission information: Important Dates: Submission Open Date: November 1, 2025 Submission Deadline: June 30, 2026 Notification of Acceptance: October 31, 2026 Manuscripts must be submitted via the Information Sciences online submission system (https://www.editorialmanager.com/ins/default.aspx). Please select the article type “VSI: Graph solutions for AI” when submitting your manuscript online. Please refer to the Guide for Authors to prepare your manuscript. All submitted papers under this call will undergo the standard review process of the journal. For any further information, the authors may contact the Guest Editors. Keywords: Graph-Based Solutions; Artificial intelligence; Deep Learning; Generative AI; Explainable AI
Last updated by Dou Sun in

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