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

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.