Massive Graph Visualization and Human-Computer Interaction for Complex Network Analysis

Networks form the backbone of data representation across scientific disciplines, capturing interactions in systems ranging from molecular biology to social science and information technology. In the life sciences, graph-based models of protein interactions, gene regulation, disease associations, and multi-omics integration have expanded dramatically in size and complexity, now often containing millions of nodes and billions of edges. Traditional visualization methods struggle with these scales, leading to information overload and loss of interpretability. Researchers therefore face dual challenges: computational, in efficiently processing and rendering such large structures, and cognitive, in enabling meaningful human analysis and interpretation. The convergence of scalable graph algorithms, visual analytics, and human-computer interaction (HCI) research offers a path forward to make massive network data comprehensible. Yet, despite advances in big graph visualization and AI-assisted analytics, the field still lacks unified frameworks that integrate interactivity, scalability, and interpretability for real-world scientific discovery.This Research Topic aims to advance the intersection of massive graph visualization and HCI by bringing together researchers from computational biology, network science, computer graphics, and data visualization. Its goal is to develop theories, algorithms, and systems that allow complex graphs to be explored interactively and intuitively, while maintaining scalability and semantic depth. By highlighting methodological innovations alongside domain-specific applications, the collection seeks to bridge the gap between algorithmic design and human sensemaking. The overarching objective is to transform how large networks are visualized, interpreted, and used to generate knowledge across domains such as bioinformatics, systems biology, and biomedical research.To gather further insights into scalable and interactive network visualization, we welcome articles addressing, but not limited to, the following themes:• Scalable algorithms for graph decomposition, clustering, and layout• Interactive and progressive visualization of large biological and biomedical networks• Visual analytics systems for multi-omics and single-cell network data• Human-in-the-loop exploration and query interfaces for massive graphs• Graph neural networks and AI-driven approaches to network visualization and interpretation• Hypergraph and higher-order representations of complex data• Network alignment, comparison, and embedding• Evaluation frameworks and user studies for graph visualization tools• Applications in drug–target networks, disease networks, and comparative genomicsWe welcome Original Research, Review, Methods, Technology and Code, and Perspective articles. Topic editors: Lei Yang, Ahmed M.S. Kheir Source: https://www.frontiersin.org/research-topics/83361/massive-graph-visualization-and-human-computer-interaction-for-complex-network-analysis