Latest Articles for Social Networks

A novel approach to manage network interference in A/B testing for better results.

A look at how relations simplify data organization and enhance analysis.

A new model enhances influence spread in social networks efficiently.

A look at new methods for understanding how ideas and technologies spread.

A new approach to graph embedding enhances data analysis through physical force concepts.

A new framework improves hyperedge prediction accuracy using context-aware aggregation and self-supervised learning.

A new estimator improves analysis of how products spread in communities.

This article presents a method to assess treatment effects, considering participant interactions.

A look at how nonequilibrium states impact biological processes and interactions.

A look into methods for aligning graphs and their importance in various fields.

A new model enhances how networks gain connections over time.

Explore new methods improving correlation clustering efficiency and accuracy.

A new system enables fast processing of temporal graphs on single machines.

This article discusses how changing connections influence the spread of information in networks.

A new framework enhances GNNs to address oversmoothing issues effectively.

This article studies maximal cliques across various random network models.

Learn how Hawkes processes explain event interactions in various fields.

This article examines the connections in friends-and-strangers graphs and their implications.

A look at transit functions and their role in clustering systems.

How network structures affect opinion sharing and belief accuracy.

A new method improves data clustering by considering complex relationships.

A look at relations, their properties, and practical applications.

An algorithm for estimating diffusion degree in real-time graph streams.

Explore Latent Graph Diffusion, a unified approach for various graph tasks.

Analyzing the evolution of fairness in social network recommendations over time.

A method to predict missing information in graph nodes using local structures.

Explore how hypergraphs capture complex relationships in social networks and biology.

Introducing message detouring for improved graph cycle representation.

Introducing a new model for predicting connections in various graph types.

Explore how sedentary vertices impact information flow in networks.

A new method that simplifies graph learning using attention.

A study on how LLMs form connections in social and professional networks.

A novel method to find maximal -plexes efficiently in large datasets.

A look at how hypergraphs improve our understanding of information flow in social networks.

SPMiner uses machine learning for efficient subgraph pattern detection in complex networks.

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

A new method to study evolving graphs and relationships over time.

Exploring the role of entropy in hyper-graphon theory and its applications.

Exploring local differential privacy methods for secure graph analysis.

A new model to analyze how biases affect opinions in social networks.