Research Progress in Complex Networks and Graph Data Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 45
Special Issue Editors
Interests: complex system; graph representation learning; machine learning
Interests: complex networks; metaheuristics; computational biology; network sciences and social networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Complex networks are mathematical representations of interconnected systems, where entities (nodes) interact through relationships (edges). These networks appear in diverse fields, from social media and biological systems to transportation and financial markets. Graph data analysis, a key tool in studying complex networks, helps uncover hidden patterns, detect communities, and model dynamic processes like information spread or epidemic outbreaks.
Techniques such as centrality measures identify influential nodes, while clustering algorithms detect tightly connected groups. Machine learning and AI further enhance graph analysis, enabling tasks like link prediction and anomaly detection. In recent years, the integration of large-scale data with graph analytics has advanced applications in recommendation systems, fraud detection, and scientific discovery.
As networks grow in size and complexity, scalable algorithms and explainable AI models are crucial for interpreting results. Understanding complex networks not only improves decision making in various domains but also provides insights into the fundamental structures governing real-world interactions.
We are looking forward to receiving your contributions and fostering meaningful discussions within complex network and graph data analysis.
Dr. Samira Maghool
Dr. Mario F. Pavone
Guest Editors
Manuscript Submission Information
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Keywords
- complex networks
- complex system
- network sciences
- graph data analysis
- machine learning
- graph representation learning
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