Latest Articles for Network Analysis

Research reveals new families of edge-girth-regular graphs with unique properties.

A look into link prediction methods and their applications across various fields.

Examining how different decision-makers interact within various network structures for stability.

A new framework to assess structural embeddings for enhanced data analysis.

New method creates large, detailed graphs for diverse applications.

This article discusses the issues and improvements in graph neural networks.

New methods to assess privacy risks in social networks, considering distant connections.

Analyzing strategies in the cops and robbers game on graphs.

A new model adapts to heterophily in graphs, improving performance and accuracy.

A new method to study how systems change over time.

A new approach to effective resistance improves analysis of point clouds.

Ramsey Theory reveals how patterns occur in large structures.

A look at the clarity and structure of axis-parallel RAC graphs.

This work introduces new rules for graph editing to achieve trivially perfect structures.

A new method for identifying maximum cliques in complex networks.

A look into the complexities and solutions for ECC and VCC problems.

This study examines cycle behavior in signed graphs through edge interactions.

Learn how minimal separators impact various fields including computer science and network analysis.

A fresh perspective on analyzing networks with node-layer duality reveals deeper insights.

A new algorithm for the temporal vertex cover problem in dynamic networks.

Exploring the role of the R'enyi index in assessing network diversity.

Recent improvements in edge coloring algorithms enhance efficiency for various graphs.

Examining how interactions over time influence disease spread in communities.

Analyzing the spectral properties of graphs reveals key insights about their connections and behaviors.

A look into Ramsey numbers and their significance in graph theory.

Examination of edge-Erdős-Pósa property in complex subcubic graphs.

A new framework balances privacy and utility in graph learning.

This study examines conditions for embedding k-factors in k-partite hypergraphs.

A new method enhances link prediction in networks with unseen relationships.

Exploring the characteristics and significance of -dicritical digraphs in graph theory.

Research links Turán's theorem with efficient algorithms for finding cliques in graphs.

This research introduces a method for detecting communities while ensuring data privacy.

This work focuses on improving reachability in directed planar graphs, considering network failures.

A look into pancyclic graphs and their significance in graph theory.

New methods create varied datasets for better evaluation of Graph Neural Networks.

A new method improves link prediction accuracy using node attributes.

Introducing a new method for efficient and high-quality graph generation.

A look into how graph partitioning aids in data analysis and clustering.

Learn about how failures spread in interdependent networks and their effects.

This article investigates the bias in Internet measurement platforms and its implications.