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

S
SegmentFault 最新的问题
MongoDB | Blog
MongoDB | Blog
J
Java Code Geeks
Stack Overflow Blog
Stack Overflow Blog
The GitHub Blog
The GitHub Blog
博客园 - Franky
博客园 - 叶小钗
大猫的无限游戏
大猫的无限游戏
V
Visual Studio Blog
IT之家
IT之家
美团技术团队
博客园 - 司徒正美
Martin Fowler
Martin Fowler
Last Week in AI
Last Week in AI
酷 壳 – CoolShell
酷 壳 – CoolShell
Vercel News
Vercel News
雷峰网
雷峰网
B
Blog
WordPress大学
WordPress大学
aimingoo的专栏
aimingoo的专栏
D
Docker
P
Proofpoint News Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
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
Interdisciplinary Relationships Between Biological and Ph...
Paulo E. P. Burke, Luciano da F. Costa · 2019-05-09 · via cs.SI updates on arXiv.org

Several interdisciplinary areas have appeared at the interface between biological and physical sciences. In this work, we suggest a complex network-based methodology for analyzing the interrelationships between some of these interdisciplinary areas, including Bioinformatics, Computational Biology, Biochemistry, among others. This approach has been applied over respective data derived from Wikipedia. Related reviews from the scientific literature are also considered as a reference, yielding a respective bipartite hypergraph which can be used to gain insights about the interrelationships underlying the considered interdisciplinary areas. Several interesting results are obtained, including greater interconnection between the considered interdisciplinary areas with biological than with physical sciences. A good agreement was also found between the network obtained from Wikipedia and the interrelationships revealed by the literature reviews. At the same time, the former network was found to exhibit more intricate relationships than in the hypergraph derived from the literature review.