The field of complex systems and human behavior research is rapidly evolving, with a growing focus on data-driven methods to understand and predict various phenomena. Recent studies have explored the application of machine learning, graph theory, and other techniques to analyze large datasets and identify patterns in human mobility, social interactions, and decision-making processes. A key direction in this field is the development of novel methods for integrating multiple data sources and modeling complex relationships between variables. For instance, researchers are using hypergraph-based approaches to capture higher-order interactions in human contact networks and improve epidemic modeling. Additionally, there is a growing interest in using causal discovery techniques to identify the underlying mechanisms driving human behavior and decision-making processes. Overall, these advances have the potential to inform policy decisions, optimize resource allocation, and improve our understanding of complex systems. Noteworthy papers include the proposal of a causality-aware framework for next location prediction and the introduction of a novel method for inferring fine-grained migration patterns across the United States.
Advances in Data-Driven Methods for Understanding Human Behavior and Complex Systems
Sources
Data-Driven Optimization of EV Charging Station Placement Using Causal Discovery
Electric Vehicle Integration using Large-Scale Combined Transmission and Distribution Grid Models
Potentials and Limitations of Large-scale, Individual-level Mobile Location Data for Food Acquisition Analysis
Evaluating Negative Sampling Approaches for Neural Topic Models
Causality-Aware Next Location Prediction Framework based on Human Mobility Stratification
Exploring Topic Trends in COVID-19 Research Literature using Non-Negative Matrix Factorization
DiffMove: Group Mobility Tendency Enhanced Trajectory Recovery via Diffusion Model
Identifying and Characterising Higher Order Interactions in Mobility Networks Using Hypergraphs
Multi-view Learning for the Identification of Risky Users in Dynamic Social Networks
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
Higher-order Interaction Matters: Dynamic Hypergraph Neural Networks for Epidemic Modeling
Dynamic Learning and Productivity for Data Analysts: A Bayesian Hidden Markov Model Perspective
Structure Identification of NDS with Descriptor Subsystems under Asynchronous, Non-Uniform, and Slow-Rate Sampling
Inferring fine-grained migration patterns across the United States
Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations
Network Density Analysis of Health Seeking Behavior in Metro Manila: A Retrospective Analysis on COVID-19 Google Trends Data
SEAGET: Seasonal and Active hours guided Graph Enhanced Transformer for the next POI recommendation
HyperMAN: Hypergraph-enhanced Meta-learning Adaptive Network for Next POI Recommendation
A Perspective on the Ubiquity of Interaction Streams in Human Realm