Latest Articles for Network Analysis

A look at directed graphs and the significance of minimal paths within them.

SCRaWl utilizes higher-order structures for better data analysis.

New methods improve efficiency in target set selection for network influence.

Explore the relationship between graph colorings and eigenvalues in mathematics.

This article examines colored graphs and their significance in various fields.

Introducing Smart, a method for better GNN performance in changing graphs.

Discover how GPU technology improves PageRank efficiency for dynamic networks.

A study on strategies in a vertex coloring game between Alice and Bob.

A new method identifies unusual patterns in evolving networks to enhance anomaly detection.

A look into how hypergraphs can model complex relationships in various systems.

Explore the significance of representation numbers in graph theory.

Introducing a method to combine biological datasets for better network reconstruction.

Analyzing strong connectivity in directed graphs reveals insights into complex systems.

Examining coordination, networks, and decision-making among agents in different settings.

An overview of degree-boundedness and its impact on graph structures.

A unified view on addressing oversmoothing challenges in Graph Neural Networks.

CoBFormer balances local and global attention for improved graph processing.

Analyzing how three-dimensional shapes influence connections in biological and physical networks.

A new model redefines graph analysis using point sets for improved predictions.

New methods improve graph communication and node distinction in GNNs.

Introducing the ACRONYM model for better community detection and edge estimation.

This study examines the Weisfeiler-Leman dimension and coherent configurations in graphs.

This study examines when monochromatic subgraph counts show normal distribution patterns.

This article examines how edge density affects triangle formation in random graphs.

A new method enhances the reliability of GCNs against adversarial changes.

A novel approach to influence maximization in complex networks.

Developing algorithms for effective triangle sampling in graph data streams.

A new model improves graph generation with discrete-state and continuous-time methods.

New methods improve efficiency in identifying communities within evolving graphs.

New concepts in graph width enhance analysis and applications in various fields.

A new approach to finding independent sets in graphs using any starting configuration.

Examining how systems sync their movements and behaviors efficiently.

Analyzing edge significance improves overall communication in various network systems.

An overview of hypermaps and their significance in mathematics and science.

A new method improves network predictions by examining node relationships through orbit adjacency.

A new method for improving graph data analysis and communication.

A new framework improves GNNs by using clustering to enhance learning.

DPHGNN enhances performance in hypergraph-related tasks using dual-layered learning.

A look into methods and challenges in predicting connections in dynamic networks.

A new approach enhances graph embeddings by focusing on dimension management.