Aims & Scope

Aims

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.

Scope

TGINA welcomes contributions from computer science, artificial intelligence, data science, network science, engineering, and interdisciplinary domains where graphs, intelligent models, and network applications play a central role.

1. Graph Intelligence

  • Graph mining and graph learning
  • Graph database systems
  • Graph neural networks (GNNs) and graph transformers
  • Graph representation learning and embeddings
  • Graph querying, matching, alignment, and similarity
  • Graph anomaly detection and structural irregularities
  • Graph signal processing and spectral graph analysis
  • Graph reasoning, inference, and decision-making
  • Graph-based multimodal fusion and integration
  • Graph dynamics and temporal graph analysis
  • Knowledge graphs and graph-augmented systems
  • Graph generative models
  • Graph trustworthiness, robustness, and interpretability
  • Graph privacy and security

2. LLMs and Intelligent Systems for Graphs & Networks

  • LLM-guided graph construction, completion, and refinement
  • LLMs enhanced retrieval on graphs and knowledge networks
  • Graph-aware LLM reasoning and generation
  • LLM-driven graph summarization and large-scale graph interpretation
  • Integration of LLMs with knowledge graphs and semantic networks
  • LLMs for networked data understanding (social, financial, biological, etc.)
  • Agent systems combining LLMs with graph intelligence and network-based decision frameworks
  • Safety, robustness, and bias analysis of LLMs operating on graph or network data
  • LLMs for multimodal networks, interconnected systems, and real-world applications
  • Ethical, responsible, and accountable use of LLMs in graph and network environments

3. Graph Computing & Networked Computation

  • Fundamental and scalable graph algorithms
  • Computation on dynamic, spatialtemporal, and heterogeneous networks
  • Cross-domain fusion of network, graph, text, and multimodal data
  • Distributed, parallel, and federated graph computation
  • Partitioning, clustering, and community detection in large networks
  • System architectures for graph and network processing
  • Hardware acceleration for graph and network workloads (GPU, TPU, heterogeneous platforms)
  • Network optimization, routing, and flow computation
  • Performance, scalability, and resource management in graph and network computation

4. Network Applications & Real-World Systems

  • Social networks, information diffusion, and influence analysis
  • Financial networks, economic systems, and risk propagation
  • Biological, biomedical, and healthcare networks
  • Neuroscience-inspired network analysis and brain-connectivity studies
  • Transportation, mobility, and logistic networks
  • Energy, communication, and IoT networks
  • Cybersecurity, anomaly detection, and fraud networks
  • Environmental, ecological, and climate-related networks
  • Industrial, manufacturing, and supply-chain networks
  • Information networks, recommendation systems, and user modeling
  • Scientific collaboration networks and innovation systems
  • Urban, infrastructural, and societal-scale network applications
  • Interdisciplinary network science and complex systems
  • Trust, governance, and responsible use of network-driven AI systems
  • Next-generation intelligent and interconnected network systems