Modeling memory in time-respecting paths on temporal networks

2025-11-21

AI Summary

Quantifying memory effects in time-respecting paths (TRPs) within temporal networks—and understanding their impact on spreading dynamics—remains challenging due to the lack of computable, interpretable metrics. Method: We propose the first tractable framework for measuring TRP memory, integrating statistical analysis of TRPs, null-model benchmarking, significance testing, and generative modeling. We develop a memory-augmented temporal graph generator and validate it systematically across diverse real-world contact datasets (e.g., school, hospital, conference). Contribution/Results: Memory effects are empirically pervasive and statistically significant across datasets. Crucially, they substantially suppress the speed of knowledge, norm, and disease propagation—contrasting sharply with memoryless assumptions underlying classical temporal network models. Our framework establishes a new paradigm for interpretable, mechanism-driven modeling of spreading processes on temporal networks, bridging theoretical abstraction with empirical observability.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Human close-range proximity interactions are the key determinant for spreading processes like knowledge diffusion, norm adoption, and infectious disease transmission. These dynamical processes can be modeled with time-respecting paths on temporal networks. Here, we propose a framework to quantify memory in time-respecting paths and evaluate it on several empirical datasets encoding proximity between humans collected in different settings. Our results show strong memory effects, robust across settings, model parameters, and statistically significant when compared to memoryless null models. We further propose a generative model to create synthetic temporal graphs with memory and use it to show that memory in time-respecting paths decreases the diffusion speed, affecting the dynamics of spreading processes on temporal networks.

Problem

• Modeling memory effects in time-respecting paths on temporal networks
• Quantifying memory in human proximity interactions across empirical datasets
• Analyzing how memory in temporal paths reduces diffusion speed

Innovation

• Framework quantifies memory in time-respecting paths
• Generative model creates synthetic temporal graphs with memory
• Memory in paths decreases diffusion speed in networks