Latest Articles for Network Analysis

Explore the fundamentals of graph theory and its real-world applications.

Exploring the significance and applications of hitting subgraphs in various fields.

Explore how topological communities enhance our understanding of complex networks.

This study tackles the evaluation issues of GNNs in heterophilic data environments.

Explore the intricacies of edge-regular graphs and their unique structures.

New algorithms improve the estimation of Kemeny’s constant for directed graphs.

A new method for studying document connections in citation networks.

Graffin enhances tail node classification in imbalanced data scenarios.

A method for tracking evolving connections in networks for better decision-making.

A study on adjusting centrality in networks for various applications.

GRE-MDCL improves graph learning by using enhanced data techniques.

This article examines how item recommendation and link prediction can work together.

New neural networks improve analysis of complex, directed data relationships.

Explore the concept of domination in graphs and its real-world applications.

A look at how to learn from networks that evolve over time.

A method for better handling large networks in machine learning.

PropEnc transforms graph metrics into useful node features, enhancing GNN performance.

Understanding how to protect the power grid from cyber attacks using graph theory.

A look at how sparse graphs relate to their line graphs and properties.

Exploring self-similarity in networks over time reveals insights into complex systems.

Exploring how trees fit into graphs through connections and structures.

A study on dynamic issues in unit disk graphs and efficient problem-solving methods.

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.