Latest Articles for Network Analysis

Explore how partitioned networks help us understand various complex relationships.

This article presents a new way to predict connections in dynamic graphs.

New methods for efficiently analyzing network changes in weighted graphs.

A look into compliant graphs and their significance in various fields.

This paper enhances federated learning for GNNs while preserving user privacy.

A look into clustered planarity and linear saturation in graph representation.

Explore the relationship between minimum degree and connectivity in graph theory.

New methods enhance counting shapes in directed graphs, improving speed and accuracy.

This article examines the unique properties of locally linear graphs and their relationships.

A look into the challenges and advancements in Minimum Flow Decomposition.

A deep dive into canonical labeling and its significance in random graph theory.

A look into graph theory and its transformations.

DA-MoE adapts GNNs for varying graph sizes, improving performance across diverse tasks.

GephiForR enhances network analysis in R with improved visualization features.

A look into Hopfield networks and their quantum-enhanced models.

Discover the intricate world of hypergraphs and their properties through unified matrices.

Research on tracking groups in shifting networks during dynamic changes.

Learn how community detection shapes networks and enhances various fields.

New strategies enhance GNN performance in multi-label node classification tasks.

Discover innovative methods for sampling signals using graph theory.

Exploring the relationships between graphons and their unique traits.

A method for better predictions in shifting data environments.

Explore how POGAT enhances the analysis of complex graph structures.

Examining the Hall ratio and fractional chromatic number in graph theory.

Exploring the relationship between graph distance metrics and shape.

Explore how different connections shape networks using causal inference.

A concise look at community detection within networks and its applications.

Discover how identifying key influencers can impact marketing and public health.

A look into how digraphs help analyze complex systems.

This article examines distances in Kronecker products of distance regular graphs.

New GCN model improves analysis of complex relationships in graph data.

A new model improves our grasp of complex networks and their interactions.

Researchers enhance GNNs to improve predictions in complex data relationships.

Explore how community recovery shapes group dynamics across multiple networks.

Discover how math helps us understand friendships and social interactions.

Inceptive Graph Neural Networks bridge the gap between homophily and heterophily for better data representation.

Learn how to spread ideas through strategic influence in social networks.

A fresh look at community detection using semi-supervised methods in networks.

Discover how random graphs shape our understanding of connections and rigidity.

Discover how random walks work in graphs and their real-life applications.