Transactions on Graph Intelligence and Network Applications
Editor-in-Chief: Prof. Jia Wu, Macquarie University, Australia.
Transactions on Graph Intelligence and Network Applications (TGINA) is an international, peer-reviewed journal dedicated to advancing theoretical foundations, algorithmic innovations, and real-world applications at the intersection of graph intelligence, large language models (LLMs), and networked systems. The journal provides a high-quality platform for disseminating emerging research that integrates graph-structured data, intelligent learning paradigms, and practical applications across diverse network environments. TGINA is published quarterly online by Scilight Press. More
Call For Papers
MoreFoundation Models and Agentic Intelligence for Graph-Centric Applications
Scope Graph-structured data play a fundamental role in modeling complex relations across social networks, recommender systems, knowledge graphs, cybersecurity, biomedicine, scientific discovery, urban computing, and enterprise intelligence. Recent advances in foundation models, large language models, and agentic intelligence are creating new opportunities for graph-centric applications by enabling large-scale pre-training, cross-domain transfer, prompt-based adaptation, semantic understanding, graph reasoning, retrieval, tool use, planning, and interactive decision-making over graph-structured data. Their convergence is opening a timely research frontier that connects graph learning, language-based intelligence, and autonomous agent systems. This aims to provide a focused venue for high-quality research on Foundation Models and Agentic Intelligence for Graph-Centric Applications. It welcomes theoretical, methodological, empirical, and system-oriented contributions that advance both directions of this emerging intersection: foundation models and agents for graph learning, reasoning, and decision-making, as well as graph-enhanced methods for improving language models, retrieval-augmented generation, autonomous agents, and knowledge-intensive AI systems. Particular emphasis will be placed on graph foundation models, graph-enhanced language models, graph-based reasoning and retrieval, graph agents, trustworthy and scalable graph-centric applications, benchmarks and evaluation protocols, and real-world applications in complex relational domains. Topics of Interest We welcome original research papers that address, but are not limited to: 1. Graph Foundation Models and Graph-Centric Pre-training Large-scale graph pre-training and graph foundation models Self-supervised, weakly supervised, and instruction-based graph learning Graph prompt learning, graph tuning, and parameter-efficient adaptation Transfer learning, domain adaptation, and continual learning on graphs Dynamic, temporal, heterogeneous, and multi-domain graph foundation models 2. Foundation Models for Graph Learning and Reasoning Large language models for graph learning, graph analytics, and graph reasoning Multimodal foundation models for graph-structured data Language-enhanced graph representation learning Foundation-model-based graph mining, prediction, and decision-making Neural, symbolic, and neuro-symbolic graph reasoning 3. Graphs for Large Language Models and Agentic Intelligence Graph-enhanced large language models and foundation models Knowledge graphs and structured knowledge for language model reasoning Graph-based context modeling, memory construction, and long-context organization Path-aware, subgraph-aware, and relation-aware reasoning with LLMs Graph-based factuality enhancement, alignment, and evaluation of language models 4. Graph Retrieval-Augmented Generation and Graph Agents Graph retrieval-augmented generation and graph-based evidence retrieval Graph agents and autonomous agents over graph-structured environments Graph-based planning, tool use, and interactive decision-making Graph world models and executable knowledge graphs for agents Multi-agent collaboration and agentic workflows over graph-structured data 5. Trustworthy, Scalable, and Deployable Graph-Centric Systems Robust, fair, explainable, and privacy-preserving graph foundation models Security, safety, and alignment of graph-enhanced AI systems Scalable training, inference, compression, and deployment Efficient graph learning systems and open-source platforms Benchmarks, datasets, evaluation protocols, and reproducibility studies 6. Foundation Models and Agentic Intelligence for Graph-Centric Applications Recommender systems, social networks, and web intelligence Cybersecurity, fraud detection, and risk analysis Biomedicine, healthcare, and scientific discovery Urban computing, transportation, and spatiotemporal intelligence Finance, enterprise intelligence, and complex decision-support systems Types of Submissions We welcome original and high-quality contributions in the following categories types: Original research papers Survey and review papers System and benchmark papers Application and industrial papers Academic Editors Prof. Qingyun Sun (sunqy@buaa.edu.cn), School of Computer Science and Engineering, Beihang University, Beijing, China. Prof. Ziwei Zhang (zwzhang@buaa.edu.cn), School of Computer Science and Engineering, Beihang University, Beijing, China. Dr. Haonan Yuan (yuanhn@buaa.edu.cn), School of Computer Science and Engineering, Beihang University, Beijing, China. Prof. Jianxin Li (lijx@buaa.edu.cn), School of Computer Science and Engineering, Beihang University, Beijing, China. Prof. Philip S. Yu (psyu@uic.edu), Department of Computer Science‌, University of Illinois at Chicago, Chicago, United States. Submission Guidelines To submit your manuscript, please go to the Transactions on Graph Intelligence and Network Applications journal website at Add Submissions - Scilight Press and follow the procedures for manuscript submission. Author Submission Guidelines can be found at: Instruction for Authors - | Transactions on Graph Intelligence and Network Applications. Please make sure to include the title of this Call for Papers in the cover letter. There is no Article Processing Charge for all submissions and accepted papers. Important Dates Submission Deadline: 31 August 2026 First Round Review Decision: 31 September 2026 Final Decision: 30 October 2026 Expected Publication: 15 November 2026 All deadlines are end-of-day in the Anywhere on Earth (AoE) time zone. All accepted papers will be published in the earliest available issue. The above timeline is provided for reference only, and the actual schedule may be subject to change. All manuscripts will be peer-reviewed in accordance with the journal's established policies and procedures. The final papers will be selected for publication depending on the results of the peer-review process and the reviews of the Academic Editors and Editor-in-Chief.
Transactions on Graph Intelligence and Network Applications
Deadline: 31 Aug 2026Trustworthy Graph Intelligence for Large Language Models and Autonomous Agents
Recent advances in Large Language Models (LLMs) and autonomous AI agents are rapidly transforming intelligent systems, networked applications, and decision-making infrastructures. Meanwhile, graph intelligence has emerged as a fundamental paradigm for modeling structured relationships, long-range dependencies, relational reasoning, and interpretable knowledge organization. The integration of graph intelligence with LLMs and autonomous agents is enabling a new generation of intelligent systems capable of memory augmentation, multi-hop reasoning, planning, coordination, and adaptive interaction in complex environments. Emerging paradigms such as GraphRAG, graph-based agent memory, multi-agent coordination graphs, and graph-guided reasoning are attracting growing attention from both academia and industry. However, despite rapid progress, critical challenges remain regarding robustness, reliability, safety, interpretability, alignment, and trustworthy deployment. Graph-enhanced intelligent systems are vulnerable to adversarial attacks, memory poisoning, hallucination propagation, unreliable retrieval, unsafe planning, and multi-agent coordination failures. Ensuring trustworthy graph intelligence has therefore become a key requirement for deploying LLMs and autonomous agents in real-world applications involving cybersecurity, healthcare, finance, recommendation systems, scientific discovery, and intelligent infrastructure. This Call for Paper (CFP) aims to provide a high-quality interdisciplinary forum for researchers and practitioners working at the intersection of graph intelligence, large language models, autonomous agents, and trustworthy AI. The issue will focus on foundational theories, algorithms, architectures, systems, evaluation methodologies, and real-world applications for trustworthy graph-enhanced intelligent systems. Topics of Interest Topics include, but are not limited to: 1. Graph-Enhanced LLMs and GraphRAG Graph foundation models Graph-enhanced large language models Knowledge graph augmented LLMs Graph Retrieval-Augmented Generation (GraphRAG) Hybrid graph-vector retrieval Multi-hop graph reasoning Structured memory augmentation Dynamic and temporal graph retrieval Graph-guided prompting and reasoning 2. Autonomous and Multi-Agent Systems Graph-based autonomous agents Agent memory graphs Planning and reasoning over graphs Multi-agent graph intelligence Communication and coordination graphs Collaborative agent reasoning Workflow and tool dependency graphs Long-horizon autonomous decision making Distributed agent systems 3. Trustworthy and Safe AI Systems Trustworthy graph intelligence Safety alignment for graph-enhanced agents Robust graph reasoning Hallucination mitigation via graph structures Safe retrieval and grounded generation Human-in-the-loop graph agents Trust calibration and reliability assessment Responsible and ethical graph AI 4. Adversarial Robustness and Security Adversarial attacks on graph-enhanced LLMs Graph poisoning and backdoor attacks Prompt injection through graph memory Multi-agent adversarial collaboration Secure graph retrieval systems Defensive architectures for graph agents Privacy-preserving graph intelligence Trust management in autonomous systems 5. Explainability, Verification, and Evaluation Explainable graph reasoning Interpretable GraphRAG systems Causal graph intelligence Formal verification for graph-agent systems Certified robustness and reliability Benchmarking trustworthy graph AI Evaluation frameworks for autonomous agents Reasoning trace analysis 6. Systems and Applications Scalable graph-agent infrastructures Real-time graph intelligence systems Efficient graph memory architectures Scientific discovery agents Recommendation and personalization Cybersecurity and fraud detection Healthcare and biomedical applications Financial network intelligence Industrial and IoT applications Types of Submissions We welcome original and high-quality contributions in the following categories: Original research papers Survey and review articles System and benchmark papers Application and industrial papers Academic Editors Prof. Xixun Lin (linxixun@iie.ac.cn), Institute of Information Engineering, Chinese Academy of Sciences, China. Dr. He Zhang (h.zhang@griffith.edu.au), Griffith University, Australia. Prof. Pengfei Cao (pengfei.cao@nlpr.ia.ac.cn), Institute of Automation, Chinese Academy of Sciences, China. Prof. Lixin Zou (zoulixin@whu.edu.cn), Wuhan University, China. Prof. Henry Nguyen (h.zhang@griffith.edu.au ), Griffith University, Australia. Submission Guidelines To submit your manuscript, please go to the Transactions on Graph Intelligence and Network Applications journal website at Add Submissions - Scilight Press and follow the procedures for manuscript submission. Author Submission Guidelines can be found at: Instruction for Authors - | Transactions on Graph Intelligence and Network Applications. Please make sure to include the title of this Call for Papers in the cover letter. There is no Article Processing Charge for all submissions and accepted papers. Important Dates Submission Deadline: 31 July 2026 First Round Review Decision: 31 August 2026 Final Decision: 30 September 2026 Expected Publication: 15 October 2026 All deadlines are end-of-day in the Anywhere on Earth (AoE) time zone. All accepted papers will be published in the earliest available issue. The above timeline is provided for reference only, and the actual schedule may be subject to change. All manuscripts will be peer-reviewed following the established policies and procedures of the journal. The final papers will be selected for publication depending on the results of the peer-review process and the reviews of the Academic Editors and Editor-in-Chief.
Transactions on Graph Intelligence and Network Applications
Deadline: 31 Jul 2026
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