Latest Articles for Network Analysis

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.

This research investigates the role of side information in identifying community structures.

This article covers how to identify complex networks with nonlinear behaviors.

A new framework improves understanding of user engagement and relationships in multiplex networks.

A look at the role of tree walks in understanding random graphs.

This paper examines how PageRank adapts to the dynamics of temporal networks.

A study on Steiner cuts and their applications in dynamic graph changes.

A look into maximal cliques and minimal transversals in graphs.

A new method to enhance network visuals by focusing on edge crossings.

Examining the influence of noise and structured signals in random matrix models.

A new method simplifies complex power systems for better analysis.

An overview of social contagion mechanisms and their impact on behavior adoption.

Explore how degree sequences affect random graph characteristics.

This study examines the presence of clique subdivisions within various graph structures.

Explore the intricacies of reachability in temporal graphs and their unique challenges.

A new model simplifies analysis of multilayer networks with diverse data types.