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

The GitHub Blog
The GitHub Blog
Y
Y Combinator Blog
B
Blog RSS Feed
大猫的无限游戏
大猫的无限游戏
J
Java Code Geeks
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
博客园 - 【当耐特】
MongoDB | Blog
MongoDB | Blog
Hugging Face - Blog
Hugging Face - Blog
有赞技术团队
有赞技术团队
T
The Blog of Author Tim Ferriss
B
Blog
小众软件
小众软件
T
Tailwind CSS Blog
MyScale Blog
MyScale Blog
I
InfoQ
Engineering at Meta
Engineering at Meta
Blog — PlanetScale
Blog — PlanetScale
P
Proofpoint News Feed
H
Help Net Security
雷峰网
雷峰网
S
SegmentFault 最新的问题
V
Visual Studio Blog
爱范儿
爱范儿

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
The Scientific Prize Network Predicts Who Pushes the Boun...
Yifang Ma, Brian Uzzi · 2018-08-29 · via cs.SI updates on arXiv.org

Scientific prizes are among the greatest recognition a scientist receives from their peers and arguably shape the direction of a field by conferring credibility to persons, ideas, and disciplines, providing financial rewards, and promoting rituals that reinforce scientific communities. The proliferation of prizes and links among prizes suggest that the prize network embodies information about scientists and ideas poised to grow in acclaim. Using comprehensive new data on prizes and prizewinners worldwide and across disciplines, we examine the growth dynamics and interlocking relationships found in the worldwide scientific prize network. We focus on understanding how the knowledge linkages among prizes and scientists' propensities for prizewinning are related to knowledge pathways across disciplines and stratification within disciplines. We find several key links between prizes and scientific advances. First, despite a proliferation of diverse prizes over time and across the globe, prizes are more concentrated within a relatively small group of scientific elites, and ties within the elites are more clustered, suggesting that a relatively constrained number of ideas and scholars lead science. Second, we find that certain prizes are strongly interlocked within and between disciplines by scientists who win multiple prizes, revealing the key pathways by which knowledge systematically gains credit and spreads through the network. Third, we find that genealogical and co authorship networks strongly predict who wins one or more prizes and explains the high level of interconnections among acclaimed scientists and their path breaking ideas.