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

爱范儿
爱范儿
量子位
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
J
Java Code Geeks
B
Blog
V
V2EX
博客园 - 三生石上(FineUI控件)
Blog — PlanetScale
Blog — PlanetScale
aimingoo的专栏
aimingoo的专栏
Y
Y Combinator Blog
F
Fortinet All Blogs
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
The Cloudflare Blog
A
About on SuperTechFans
D
DataBreaches.Net
阮一峰的网络日志
阮一峰的网络日志
博客园 - Franky
H
Help Net Security
宝玉的分享
宝玉的分享
Martin Fowler
Martin Fowler
酷 壳 – CoolShell
酷 壳 – CoolShell
MongoDB | Blog
MongoDB | Blog
L
LangChain Blog

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
Spectral Perturbation and Reconstructability of Complex N...
D. Liu, H. Wang, P. Van Mieghem · 2010-10-22 · via cs.SI updates on arXiv.org

In recent years, many network perturbation techniques, such as topological perturbations and service perturbations, were employed to study and improve the robustness of complex networks. However, there is no general way to evaluate the network robustness. In this paper, we propose a new global measure for a network, the reconstructability coefficient θ, defined as the maximum number of eigenvalues that can be removed, subject to the condition that the adjacency matrix can be reconstructed exactly. Our main finding is that a linear scaling law, E[θ]=aN, seems universal, in that it holds for all networks that we have studied.