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

钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园_首页
Engineering at Meta
Engineering at Meta
量子位
A
About on SuperTechFans
阮一峰的网络日志
阮一峰的网络日志
Recent Announcements
Recent Announcements
博客园 - 司徒正美
V
Visual Studio Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
The GitHub Blog
The GitHub Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
F
Fortinet All Blogs
Martin Fowler
Martin Fowler
腾讯CDC
Jina AI
Jina AI
C
Check Point Blog
H
Help Net Security
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
V2EX
爱范儿
爱范儿
I
InfoQ

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
Homophily of Music Listening in Online Social Networks
Zhenkun Zhou, Ke Xu, Jichang Zhao · 2017-10-30 · via cs.SI updates on arXiv.org

Homophily, ranging from demographics to sentiments, breeds connections in social networks, either offline or online. However, with the prosperous growth of music streaming service, whether homophily exists in online music listening remains unclear. In this study, two online social networks of a same group of active users are established respectively in Netease Music and Weibo. Through presented multiple similarity measures, it is evidently demonstrated that homophily does exist in music listening of both online social networks. The unexpected music similarity in Weibo also implies that knowledge from generic social networks can be confidently transfered to domain-oriented networks for context enrichment and algorithm enhancement. Comprehensive factors that might function in formation of homophily are further probed and many interesting patterns are profoundly revealed. It is found that female friends are more homogeneous in music listening and positive and energetic songs significantly pull users close. Our methodology and findings would shed lights on realistic applications in online music services.