The scope includes the knowledge and data engineering aspects of computer science, artificial intelligence, electrical engineering, computer engineering, and other appropriate fields. This Transactions provides an international and interdisciplinary forum to communicate results of new developments in knowledge and data engineering and the feasibility studies of these ideas in hardware and software. Specific areas to be covered are as follows: Fields and Areas of Knowledge and Data Engineering: (a) Knowledge and data engineering aspects of knowledge based and expert systems, (b) Artificial Intelligence techniques relating to knowledge and data management, (c) Knowledge and data engineering tools and techniques, (d) Distributed knowledge base and database processing, (e) Real-time knowledge bases and databases, (f) Architectures for knowledge and data based systems, (g) Data management methodologies, (h) Database design and modeling, (i) Query, design, and implementation languages, (j) Integrity, security, and fault tolerance, (k) Distributed database control, (l) Statistical databases, (m) System integration and modeling of these systems, (n) Algorithms for these systems, (o) Performance evaluation of these algorithms, (p) Data communications aspects of these systems, (q) Applications of these systems.

Special Issue on Graph Foundation Models: Database and Data Mining Perspectives (GFM)
Submission Date: 2026-03-30

Graphs, which encapsulate the complex intercorrelation among objects, are ubiquitous non-Euclidean structures, found in domains ranging from recommender systems and social media analysis to financial technology and drug discovery. With the explosion of data, graphs are becoming increasingly large and complex. Neural graph databases have been introduced to manage large-scale graphs while enabling graph inference with graph neural networks. Recently, foundation models, such as Large Language Models (LLMs), have marked a revolutionary advancement in addressing numerous tasks using universally pretrained models. Graph data and inference tasks are diverse; however, unlike the success in the language and vision domains, foundation models remain in their infancy in the graph domain.

Graph Foundation Models (GFM) refer to a novel family of general-purpose graph models that are pre-trained at scale on diverse graph data, providing new challenges in both graph mining and graph database domains. Recent advances on GFM have explored leveraging LLMs to build GFMs; however, this line of work often struggles with the graph inference, particularly when complex structural patterns are involved. Other efforts design GFM using graph neural networks, yet fundamental challenges hinder their scalability. Key open issues include managing large-scale graphs, enabling distributed training of graph models, improving graph knowledge transferability and accelerating both LLM and graph inference. Addressing these challenges makes the discussions of GFM both urgent and timely.

This special issue aims to bring together researchers and practitioners from academia and industry to present their latest findings related on graph mining, graph databases, and LLMs with particular emphasis on graph foundation models. We invite submissions of papers that address fundamental issues, proposed novel models, or showcase compelling applications that shed light on the next-generation graph learning paradigm.

Topics and Scopes

This special issue will cover a wide range of topics on graph foundation models, including but not limited to:

1. Large Language Model and Graphs

    Clique counting with LLMs and graph reasoning agent
    Graph tokenization, Mixture-of-Experts, Mixture of Thought prompting
    Graph post-training, instruction tuning, prompting and in-context reasoning
    Hybrid GNN–LLM architectures and graph-language co-training
    Graph in-context learning and graph retrieval-augmented generation

2. Algorithms for Graphs and Geometries

    Riemannian/Non-Euclidean graph models
    Methodologies for heterophilic, heterogeneous, directed or imbalanced graphs
    Graph and structure generation
    Geometrical and topological analysis on symmetry and equivariance
    Acceleration for graph query and graph inference
    Model quantification, graph topological pattern injection, and graph knowledge distillation

3. Graph Database and Management of Billion-scale Graphs

    Graph database and neural graph databases
    Sparsification and sampling methods for graphs
    Distributed and decentralized graph training
    Scalable and efficient graph transformers

4. Knowledge Transfer among Graphs

    Cross-domain and few-shot graph knowledge transfer
    Domain alignment in pretraining
    Parameter-efficient graph fine-tuning (LoRA, adapters)
    Prototype-based adaptation and test-time tuning for graphs

5. Trustworthy and Privacy on Graphs

    Privacy-preserving graph neural networks
    Adversarial attacks and graph poisoning
    Explainability and interpretability on graph engineering
    Causality and counterfactual learning on graphs

6. Real-world Applications 

    Knowledge base and knowledge graphs
    Drug discovery and models for molecular graphs
    Financial transaction network analysis
    Dynamic interacting systems and multiagent systems
    Management of spatio-temporal graphs and transportation systems

7. Datasets and Benchmarking

    Synthetic graph corpus generation
    Graph foundation model benchmarking protocols
    Open-world heterogeneous GFM benchmarks