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

Google DeepMind News
Google DeepMind News
C
Check Point Blog
J
Java Code Geeks
腾讯CDC
Martin Fowler
Martin Fowler
MongoDB | Blog
MongoDB | Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园 - 三生石上(FineUI控件)
Apple Machine Learning Research
Apple Machine Learning Research
大猫的无限游戏
大猫的无限游戏
Engineering at Meta
Engineering at Meta
罗磊的独立博客
Last Week in AI
Last Week in AI
B
Blog
IT之家
IT之家
S
SegmentFault 最新的问题
D
DataBreaches.Net
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
GbyAI
GbyAI
博客园 - 聂微东
U
Unit 42
有赞技术团队
有赞技术团队
Y
Y Combinator Blog
MyScale Blog
MyScale 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
Measuring Technological Complexity Using Japanese Patents
Rintaro Karashima, Hiroyasu Inoue · 2025-04-16 · via cs.SI updates on arXiv.org

As international competition intensifies in technologies, nations need to identify key technologies to foster innovation. However, the identification is challenging due to the independent and inherently complex nature of technologies. Traditionally, analyses of technological portfolios have been limited to simple evaluations, indicating merely whether a technology is specialized. We propose evaluating TCI at the corporate level, which provides finer granularity and more detailed insights than conventional regional evaluations by using Japanese patent data spanning fiscal years 1981 to 2010. Specifically, we analyze a bipartite network composed of 1,939 corporations connected to technological fields categorized into either 35 or 124 classifications. Our findings quantitatively characterize the ubiquity and sophistication of each technological field, reveal detailed technological trends reflecting broader societal contexts, and demonstrate methodological stability even when employing finer technological classifications. Additionally, our corporate-level analysis allows consistent comparisons across different regions and technological fields, clarifying regional advantages in specific technologies. The corporate level analysis also reveals a new possibility for formulating an innovation strategy.