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
腾讯CDC
博客园 - 司徒正美
A
About on SuperTechFans
H
Help Net Security
J
Java Code Geeks
C
Check Point Blog
B
Blog RSS Feed
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
MongoDB | Blog
MongoDB | Blog
U
Unit 42
Hugging Face - Blog
Hugging Face - Blog
Last Week in AI
Last Week in AI
MyScale Blog
MyScale Blog
V
Visual Studio Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
I
InfoQ
H
Hackread – Cybersecurity News, Data Breaches, AI and More
F
Fortinet All Blogs
博客园 - 聂微东
酷 壳 – CoolShell
酷 壳 – CoolShell
GbyAI
GbyAI
博客园 - 【当耐特】
雷峰网
雷峰网

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
Computational Human Dynamics
Márton Karsai · 2019-07-17 · via cs.SI updates on arXiv.org

This thesis summarises my scientific contributions in the domain of network science, human dynamics and computational social science. These contributions are associated to computer science, physics, statistics, and applied mathematics. The goal of this thesis is twofold, on one hand to write a concise summary of my most interesting scientific contributions, and on the other hand to provide an up-to-date view and perspective about my field. I start my dissertation with an introduction to position the reader on the landscape of my field and to put in perspective my contributions. In the second chapter I concentrate on my works on bursty human dynamics, addressing heterogeneous temporal characters of human actions and interactions. Next, I discuss my contributions to the field of temporal networks and give a synthesises of my works on various methods of the representation, characterisation, and modelling of time-varying structures. Finally, I discuss my works on the data-driven observations and modelling of collective social phenomena. There, I summarise studies on the static observations of emergent patterns of socioeconomic inequalities and their correlations with social-communication networks, and with linguistic patterns. I also discuss dynamic observations and modelling of social contagion processes.