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

Jina AI
Jina AI
大猫的无限游戏
大猫的无限游戏
Microsoft Security Blog
Microsoft Security Blog
C
Check Point Blog
云风的 BLOG
云风的 BLOG
J
Java Code Geeks
阮一峰的网络日志
阮一峰的网络日志
MongoDB | Blog
MongoDB | Blog
Engineering at Meta
Engineering at Meta
H
Help Net Security
Microsoft Azure Blog
Microsoft Azure Blog
Recent Announcements
Recent Announcements
Google DeepMind News
Google DeepMind News
F
Fortinet All Blogs
宝玉的分享
宝玉的分享
H
Hackread – Cybersecurity News, Data Breaches, AI and More
腾讯CDC
A
About on SuperTechFans
酷 壳 – CoolShell
酷 壳 – CoolShell
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
S
SegmentFault 最新的问题
Vercel News
Vercel News
aimingoo的专栏
aimingoo的专栏
B
Blog RSS Feed

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
How to efficiently destroy a network with limited informa...
T. M. Vieira, G. M. Viswanathan, L. R. da Silva · 2014-11-10 · via cs.SI updates on arXiv.org

We address the general problem of how best to attack and destroy a network by node removal, given limited or no prior information about the edges. We consider a family of strategies in which nodes are randomly chosen, but not removed. Instead, a random acquaintance (i.e., a first neighbour) of the chosen node is removed from the network. By assigning an informal cost to the information about the network structure, we show using cost-benefit analysis that acquaintance removal is the optimal strategy to destroy networks efficiently.