social-networks

This paper presents an unsupervised graph neural network framework for solving the Minimum Dominating Set problem, achieving significant speed improvements and generalization for influence maximization in social networks.

This paper examines how LLM-based agents construct human stereotypes on an open social platform, finding that bias manifests as a dynamic discourse process rather than isolated model outputs.

This paper presents a valence-aware social-network framework using computer vision to analyze interactions in dairy cattle, revealing contrasting affiliative and agonistic network structures that emphasize the importance of interaction valence in social network analysis.

The article summarizes research showing that who you know, especially weak ties, is a stronger predictor of success than hard work or luck, citing studies on Facebook friendships and LinkedIn job mobility, and recommends a tool for finding such contacts.

Proposes TENSOR, an unsupervised anomaly detection method for identifying information operations users by analyzing temporal behavioral and language patterns using temporal point processes and LLM responses. Outperforms baselines on five real-world datasets.

This paper models a question-answering forum staffed by expert knowledge workers, studying optimal scheduling to maximize system capacity and stability.

This paper presents a contextual multi-armed bandit framework that learns individual spillover probabilities in social networks to optimize stimulated word-of-mouth marketing, achieving higher rewards by targeting connected users.

This study analyzes how personality, model backbone, and guardrails influence the emergent social behavior of deployed AI agents on the Moltbook platform, finding that personality specification is the dominant factor.