Latest Articles for Network Analysis

Innovative methods enhance website identification through network traffic analysis.

New framework allows for efficient removal of sensitive data from Graph Neural Networks.

Explore the significance and properties of degree-similar graphs in various applications.

A novel method to detect changes in network structures as they happen.

Introducing CNES for efficient link prediction in changing networks.

Exploring how properties of random graphs affect their structure and connectivity.

Learn how graph embedding can improve analysis of complex networks.

A new method enhances graph clustering accuracy and efficiency.

SignedLouvain improves detection of communities in networks with positive and negative relationships.

A new method enhances detection of malicious activities in computer networks.

A fresh approach to visualize complex networks effectively.

Learn how networks change over time using the Temporal Configuration Model.

A clear look into Slupecki digraphs and their unique properties.

Efficient methods to reconstruct graphs using minimal queries in large networks.

Analyzing neural motifs in C. elegans reveals insights into nervous system function.

This article discusses a new method for classifying graphs using basic structural features.

Examining how behaviors spread using hypergraph models.

Study community formation in networks to gain insights into social behavior.

GrassNet uses State Space Models for adaptive filtering in graph neural networks.

Research improves cluster connectivity in community detection via Stochastic Block Models.

Learn how distance oracles optimize distance queries in large networks.

A new approach enhances graph clustering by combining structural and attribute information.

Learn about sampling methods to study large networks effectively.

Introducing a flexible model for analyzing changing relationships in networks.

This research examines how k-regular graphs behave under percolation.

A new method improves link prediction in graph neural networks using LLwLC.

A study of labeling techniques in null and 1-regular graphs.

Learn about edge geodetic graphs and their significance in network connections.

Research focuses on efficient algorithms for dominating sets in complex networks.

A new method improves synthetic networks to better reflect real-world communities.

Learn about new techniques for efficient biharmonic distance estimation in networks.

A study of random hypergraphs, focusing on subgraph counts and statistical behavior.

Explore the structure and properties of directed graphs and their applications.

A look into locating-chromatic numbers and their significance in graph coloring.

A new method to detect odd-length cycles in graphs using advanced algorithms.

A new method for comparing complex data structures in graphs and networks.

A study of effective network interventions in public administration and their impact.

Learn how quantum methods can improve path counting in directed graphs.

New methods enhance community detection in various network types.

A method to clarify complex graphs for analysis and understanding.