Latest Articles for Network Analysis

A look into graph coloring strategies and Ramsey theory's implications.

A new method for assessing data worth in complex graph data.

New framework enhances data selection and graph cleaning in noisy environments.

Examines the complexity of key graph problems with specific parameters.

Exploring new methods to efficiently address the domination problem in complex graphs.

This study examines privacy concerns in graph representation learning and edge reconstruction attacks.

A new method offers clearer insights into GNN predictions without extra training.

A2GNN model enhances performance in adapting graph knowledge across different domains.

HPLC enhances link prediction in graphs through landmark selection and clustering.

Explore the complexities of directed graphs with vertex weights and edge ideals.

Examining methods to lower centrality scores and reduce influence in networks.

Learn about graphs, eccentricity, centrality, and their applications.

A new approach improves link prediction accuracy using a mixture of expert models.

An overview of subgraph connectivity and sensitivity oracles in dynamic graphs.

This article examines a method for assessing uncertainty in network statistics.

Investigating methods for understanding unknown systems through partial information.

This research examines the conditions for loose Hamilton cycles in random hypergraphs.

HeiStreamE and FreightE tackle large graph edge partitioning efficiently.

Hyperedge Augmentation enhances GNNs by capturing complex relationships in data.

A method for finding the shortest path while considering road faults.

An overview of approximating triangles in graphs within distributed networks.

Exploring the complexities of maintaining community features while reducing network size.

Examining the complexities of connecting points in a graph without overlapping paths.

A look at the role of Turan and Ramsey numbers in hypergraphs.

A new method enhances network understanding by combining node features with structure.

Explore the dynamics of points in hypergraphs through strategic connections.

Introducing a method to improve predictions in changing graph data environments.

GSL-LPA enhances community detection in large networks by maintaining connectivity.

A new method for comparing temporal graphs efficiently.

A look into matching algorithms and their performance in stochastic block models.

A new model improves link prediction in multiplex social networks.

Exploring the complexities and significance of digraphs in various contexts.

Analyzing four-valent oriented graphs and their symmetry properties.

Learn how GKEDM improves Graph Convolutional Network performance.

A new method improves finding large defective cliques in complex graphs.

Explore GWSBM and GWPDSM models in community detection.

A new method simplifies the study of random geometric graphs for deeper insights.

Exploring the applications and algorithms of minimal spanning arborescences in various fields.

New methods reveal community structures in networks, enhancing insights in various fields.

This work focuses on improving link prediction accuracy with innovative sampling methods.