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Advances in Graph Learning and Representation Models for Complex Network...
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Advances in Graph Learning and Representation Models for Complex Network Analysis

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1360

Special Issue Editors

Dr. Enrico Corradini

E-Mail Website
Guest Editor
Department of Information Engineering (DII), Marche Polytechnic University, Ancona, Italy
Interests: data science; social and complex network analysis; graph mining; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Cauteruccio

E-Mail Website
Guest Editor
Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Salerno, Italy
Interests: social and complex network analysis; data mining and data science; Internet of Things; logic programming and methods for coupling inductive and deductive reasoning; advanced algorithms for sequences comparison
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Graph-structured data pervade numerous domains, underpinning systems and processes ranging from social and communication networks to biological pathways, financial systems, recommendation engines, and knowledge graphs. Effectively learning from and analyzing such data remains a central challenge in machine learning and network science. Recent progress in graph representation learning, such as transformer-based architectures, has significantly advanced our ability to model complex relational structures, capture long-range dependencies, and generalize across diverse graph types.

This Special Issue aims to bring together leading-edge research that explores the design, implementation, and application of graph learning and representation model. We seek to highlight both foundational innovations and practical insights that push the boundaries of what is possible in graph-based learning, with particular attention to model scalability, interpretability, and cross-domain utility.

We invite original research articles and comprehensive survey papers addressing, but not limited to, the following topics:

  • Novel Graph Transformer Architectures;
  • Applications of Graph Transformers;
  • Scalable and Efficient Training;
  • Interpretability and Explainability;
  • Heterogeneous and Multi-Relational Graphs;
  • Higher-order network representation and learning;
  • Generative and Predictive Applications;
  • Benchmarking and Evaluation;
  • Ethical, Privacy, and Security Considerations.

Our aim is to assemble high-impact contributions across these topics to create a Special Issue that serves as a comprehensive resource for researchers and practitioners working at the intersection of graph learning and complex network analysis. We encourage submissions that not only demonstrate technical novelty but also articulate the broader implications of their work for science, society, and industry.

We look forward to receiving your contributions.

Dr. Enrico Corradini
Dr. Francesco Cauteruccio
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. Big Data and Cognitive Computing 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 1800 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 transformers
  • self-attention on graphs
  • heterogeneous and multi-relational graphs
  • scalable graph learning
  • interpretability and explainability
  • real-time graph analytics
  • privacy-preserving graph methods

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

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Research

25 pages, 1360 KB  
Article
Source Robust Non-Parametric Reconstruction of Epidemic-like Event-Based Network Diffusion Processes Under Online Data
by Jiajia Xie, Chen Lin, Xinyu Guo and Cassie S. Mitchell
Big Data Cogn. Comput. 2025, 9(10), 262; https://doi.org/10.3390/bdcc9100262 - 16 Oct 2025
Viewed by 286
Abstract
Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in [...] Read more.
Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in real time under conditions of missing and evolving data. A novel non-parametric reconstruction method by simple weights differentiationis proposed to enhance source detection robustness with provable improved error bounds. The approach introduces adaptive cost adjustments, dynamically reducing high-risk source penalties and enabling bounded detours to mitigate errors introduced by missing edges. Theoretical analysis establishes enhanced upper bounds on false positives caused by detouring, while a stepwise evaluation of dynamic costs minimizes redundant solutions, resulting in robust Steiner tree reconstructions. Empirical validation on three real-world datasets demonstrates a 5% improvement in Matthews correlation coefficient (MCC), a twofold reduction in redundant sources, and a 50% decrease in source variance. These results confirm the effectiveness of the proposed method in accurately reconstructing temporal network diffusion while improving stability and reliability in both offline and online settings. Full article
(This article belongs to the Special Issue Advances in Graph Learning and Representation Models for Complex Network Analysis)
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24 pages, 1550 KB  
Article
Tester-Guided Graph Learning with End-to-End Detection Certificates for Triangle-Based Anomalies
by Manuel J. C. S. Reis
Big Data Cogn. Comput. 2025, 9(10), 257; https://doi.org/10.3390/bdcc9100257 - 12 Oct 2025
Viewed by 306
Abstract
We investigate anomaly detection in complex networks through a property-testing-guided graph neural model (PT-GNN) that provides an end-to-end miss-probability certificate (δ+α). The method combines (i) a wedge-sampling tester that estimates triangle-closure frequency and derives a concentration bound [...] Read more.
We investigate anomaly detection in complex networks through a property-testing-guided graph neural model (PT-GNN) that provides an end-to-end miss-probability certificate (δ+α). The method combines (i) a wedge-sampling tester that estimates triangle-closure frequency and derives a concentration bound (δ) via Bernstein’s inequality, with (ii) a lightweight classifier over structural features whose validation error contributes (α). The overall certificate is given by the sum (δ+α), quantifying the probability of missed anomalies under bounded sampling. On synthetic communication graphs with n = 1000, edge probability p = 0.01, and anomalous subgraph size k = 120, PT-GNN achieves perfect detection performance (AUC = 1.0, F1 = 1.0) across all tested regimes. Moreover, the miss-probability certificate tightens systematically as the tester budget m increases (e.g., for ε = 0.06, enlarging m from 2000 to 8000 reduces (δ+α) from ≈0.87 to ≈0.49). These results demonstrate that PT-GNN effectively couples graph learning with property testing, offering both strong empirical detection and formally verifiable guarantees in anomaly detection tasks. Full article
(This article belongs to the Special Issue Advances in Graph Learning and Representation Models for Complex Network Analysis)
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33 pages, 20640 KB  
Article
A Complex Network Science Perspective on Urban Parcel Locker Placement
by Enrico Corradini, Mattia Mandorlini, Filippo Mariani, Paolo Roselli, Samuele Sacchetti and Matteo Spiga
Big Data Cogn. Comput. 2025, 9(10), 249; https://doi.org/10.3390/bdcc9100249 - 30 Sep 2025
Viewed by 462
Abstract
The rapid rise of e-commerce is intensifying pressure on last-mile delivery networks, making the strategic placement of parcel lockers an urgent urban challenge. In this work, we adapt multilayer two-mode Social Network Analysis to the parcel-locker siting problem, modeling city-scale systems as bipartite [...] Read more.
The rapid rise of e-commerce is intensifying pressure on last-mile delivery networks, making the strategic placement of parcel lockers an urgent urban challenge. In this work, we adapt multilayer two-mode Social Network Analysis to the parcel-locker siting problem, modeling city-scale systems as bipartite networks linking spatially resolved demand zones to locker locations using only open-source demographic and geographic data. We introduce two new Social Network Analysis metrics, Dual centrality and Coverage centrality, designed to identify both structurally critical and highly accessible lockers within the network. Applying our framework to Milan, Rome, and Naples, we find that conventional coverage-based strategies successfully maximize immediate service reach, but tend to prioritize redundant hubs. In contrast, Dual centrality reveals a distinct set of lockers whose presence is essential for maintaining overall connectivity and resilience, often acting as hidden bridges between user communities. Comparative analysis with state-of-the-art multi-criteria optimization baselines confirms that our network-centric metrics deliver complementary, and in some cases better, guidance for robust locker placement. Our results show that a network-analytic lens yields actionable guidance for resilient last-mile locker siting. The method is reproducible from open data (potential-access weights) and plug-in compatible with observed assignments. Importantly, the path-based results (Coverage centrality) are adjacency-driven and thus largely insensitive to volumetric weights. Full article
(This article belongs to the Special Issue Advances in Graph Learning and Representation Models for Complex Network Analysis)
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