Latest Articles for Network Analysis

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.

DeepHGNN enhances hierarchical forecasting accuracy using graph neural networks.

A look into non-distributive modal logic and its implications in reasoning.

New framework improves predictions by analyzing interconnected data series.

This article examines security risks of backdoor attacks on machine learning in graph systems.

A look into how random graphs reveal growth patterns and properties.

New methods improve efficiency for identifying dense subgraphs in large datasets.

A method for predicting responses in interconnected networks using shared underlying structures.

A new method for identifying key influencers in hypergraphs shows promising results.

A comprehensive tool for modeling spreading phenomena in complex networks.

Learn how graph randomization helps analyze network behavior and patterns.

This study evaluates methods for finding communities in bipartite signed networks.

A look at how color changes in graphs through a probabilistic process with reversion.

Examining the properties and applications of metric graphs, focusing on -metric and hyperbolicity.

A new benchmark helps improve GNN performance amid label noise challenges.

A look into the connections and structures within the permutahedron.

This article presents an effective approach to identifying short paths in complex networks.

A look into saturated hypergraphs and their significance in random networks.

New model enhances the study of changing relationships over time.

A new dataset enhances prediction capabilities in temporal graph neural networks.

Learn how to update strongly connected components in dynamic planar graphs.

A new benchmark addresses biases in link prediction evaluations for better real-world applications.

A new approach to finding dense subnetworks based on temporal motifs.

This study reveals the properties and applications of normal matrices and balanced graphs.

Exploring the significance of spectral radii in graphs and their applications.

Fixed nodes in directed acyclic graphs are vital for network control.