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

J
Java Code Geeks
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Blog — PlanetScale
Blog — PlanetScale
G
Google Developers Blog
Microsoft Security Blog
Microsoft Security Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
腾讯CDC
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Jina AI
Jina AI
雷峰网
雷峰网
T
Tailwind CSS Blog
爱范儿
爱范儿
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
大猫的无限游戏
大猫的无限游戏
月光博客
月光博客
博客园 - 司徒正美
I
InfoQ
Engineering at Meta
Engineering at Meta
Vercel News
Vercel News
小众软件
小众软件
U
Unit 42
Google DeepMind News
Google DeepMind News
D
DataBreaches.Net

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
Dynamics of periodic node states on a model of static net...
Suhan Ree · 2012-03-15 · via cs.SI updates on arXiv.org

We introduce a simple model of static networks, where nodes are located on a ring structure, and two accompanying dynamic rules of repeated averaging on periodic node states. We assume nodes can interact with neighbors, and will add long-range links randomly. The number of long-range links, E, controls structures of these networks, and we show that there exist many types of fixed points, when E is varied. When E is low, fixed points are mostly diverse states, in which node states are diversely populated; on the other hand, when E is high, fixed points tend to be dominated by converged states, in which node states converge to one value. Numerically, we observe properties of fixed points for various E's, and also estimate points of the transition from diverse states to converged states for four different cases. This kind of simple network models will help us understand how diversities that we encounter in many systems of complex networks are sustained, even when mechanisms of averaging are at work,and when they break down if more long-range connections are added.