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

Microsoft Security Blog
Microsoft Security Blog
J
Java Code Geeks
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
L
LangChain Blog
I
InfoQ
D
Docker
F
Fortinet All Blogs
Y
Y Combinator Blog
Martin Fowler
Martin Fowler
月光博客
月光博客
B
Blog
Engineering at Meta
Engineering at Meta
T
Tailwind CSS Blog
罗磊的独立博客
博客园_首页
G
Google Developers Blog
Stack Overflow Blog
Stack Overflow Blog
Recent Announcements
Recent Announcements
D
DataBreaches.Net
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog RSS Feed
IT之家
IT之家
V
V2EX

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
Mapping Philanthropic Support of Science
Louis M. Shekhtman, Alexander J. Gates, Albert-László Barabási · 2022-06-10 · via cs.SI updates on arXiv.org

While philanthropic support for science has increased in the past decade, there is limited quantitative knowledge about the patterns that characterize it and the mechanisms that drive its distribution. Here, we map philanthropic funding to universities and research institutions based on IRS tax forms from 685,397 non-profit organizations. We identify nearly one million grants supporting institutions involved in science and higher education, finding that in volume and scope, philanthropic funding has grown to become comparable to federal research funding. Yet, distinct from government support, philanthropic funders tend to focus locally, indicating that criteria beyond research excellence play an important role in funding decisions. We also show evidence of persistence, i.e., once a grant-giving relationship begins, it tends to continue in time. Finally, we leverage the bipartite network of supporters and recipients to help us demonstrate the predictive power of the underlying network in foreseeing future funder-recipient relationships. The developed toolset could offer funding recommendations to organizations and help funders diversify their portfolio. We discuss the policy implications of our findings for philanthropic funders, individual researchers, and quantitative understanding of philanthropy.