Advanced Graph Neural Networks

Discover maximal balanced quasi-clique enumeration for signed graphs. A novel NP-hard algorithm identifies cohesive subgraphs with positive and negative edges using branch-and-bound optimization.

Framework for testing conditional and unconditional independence between binary graphs using community correlations and graph encoder embeddings.

Theoretical analysis explains why climate foundation models succeed at field prediction but fail at extreme event detection through in-context learning complexity.

Survey of graph-based techniques in retrieval-augmented generation systems, examining their roles in database construction, algorithms, and reasoning with structured knowledge.

Study proposes learnable communication graphs for multi-agent systems, enabling dynamic information sharing that adapts to task demands and reduces computational resource consumption.

GeoGraphNetworks: 110 validated spatial networks spanning US and UK transportation and hydrological systems in analysis-ready JSON and XLSX formats with complete topological and geographic data.

Systematic mapping of human-centric evaluation approaches for semantic resources like ontologies and knowledge graphs, synthesizing 15 years of research into a theoretical framework.

CORE applies Information Bottleneck principles to augment graph data for link prediction, simultaneously recovering missing edges and reducing noise to enhance model robustness.

Prompt-driven framework combining LLMs with knowledge graphs for reliable knowledge-based question answering through structured subgraph retrieval and stepwise reasoning validation.

Graph matching framework for cross-modality intravascular ultrasound and optical coherence tomography sequence registration with simultaneous temporal and rotational alignment.