惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
L
LangChain Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
罗磊的独立博客
J
Java Code Geeks
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 叶小钗
小众软件
小众软件
博客园 - Franky
D
Docker
Google DeepMind News
Google DeepMind News
Microsoft Azure Blog
Microsoft Azure Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
U
Unit 42
宝玉的分享
宝玉的分享
C
Check Point Blog
B
Blog
V
V2EX
博客园 - 三生石上(FineUI控件)
MyScale Blog
MyScale Blog
The Cloudflare Blog
博客园 - 聂微东
博客园_首页
Engineering at Meta
Engineering at Meta

cs.SI updates on arXiv.org

Hiding in Plain Sight: Finding MAHA on Reddit Prism: Structural Symmetry Scanning via Duality-Constrained Laplacian Projection MV-Gate: Insider Threat Detection via Multi-View Behavioral Statistics and Semantic Modeling Algorithmic Cultivation: How Social Media Feeds Shape User Language Universal Dynamics of Punctuated Progress AI-Mediated Communication Can Steer Collective Opinion CitePrism: Human-in-the-Loop AI for Citation Auditing and Editorial Integrity Explainable Detection of Depression Status Shifts from User Digital Traces Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles Humanwashing -- It Should Leave You Feeling Dirty When Do LLMs Generate Realistic Social Networks? A Multi-Dimensional Study of Culture, Language, Scale, and Method Moltbook Moderation: Uncovering Hidden Intent Through Multi-Turn Dialogue Linking Extreme Discourse to Structural Polarization in Signed Interaction Networks Predicting Channel Closures in the Lightning Network with Machine Learning Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence When Can Digital Personas Reliably Approximate Human Survey Findings? RAwR: Role-Aware Rewiring via Approximate Equitable Partition GravityGraphSAGE: Link Prediction in Directed Attributed Graphs Structure-Centric Graph Foundation Model via Geometric Bases Attention-based graph neural networks: a survey When AI Meets Science: Research Diversity, Interdisciplinarity, Visibility, and Retractions across Disciplines in a Global Surge Scalable inference of spatial regions and temporal signatures from time series Can LLMs Emulate Human Belief Dynamics? Predicting Post Virality with Temporal Cross-Attention over Trend Signals H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems Spectral Graph Sparsification Preserves Representation Geometry in Graph Neural Networks
In A Society of Strangers, Kin Is Still Key: Identified F...
Tamás Dávid-Barrett, Sebastian Diaz, Carlos Rodriguez-Sickert, I · 2023-07-07 · via cs.SI updates on arXiv.org

Mobile call networks have been widely used to investigate communication patterns and the network of interactions of humans at the societal scale. Yet, more detailed analysis is often hindered by having no information about the nature of the relationships, even if some metadata about the individuals are available. Using a unique, large mobile phone database with information about individual surnames in a population in which people inherit two surnames: one from their father, and one from their mother, we are able to differentiate among close kin relationship types. Here we focus on the difference between the most frequently called alters depending on whether they are family relationships or not. We find support in the data for two hypotheses: (1) phone calls between family members are more frequent and last longer than phone calls between non-kin, and (2) the phone call pattern between family members show a higher variation depending on the stage of life-course compared to non-family members. We give an interpretation of these findings within the framework of evolutionary anthropology: kinship matters even when demographic processes, such as low fertility, urbanisation and migration reduce the access to family members. Furthermore, our results provide tools for distinguishing between different kinds of kin relationships from mobile call data, when information about names are unavailable.