Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution

2025-11-30

AI Summary

Existing network evolution models struggle to disentangle and quantify the dynamic, relative contributions of multiple co-occurring generative mechanisms in real-world temporal networks. Method: We propose an event-level hybrid mechanism inference framework that employs graph neural networks (GNNs) to enhance conditional density estimation, enabling Bayesian approximate inference of mechanism-specific weights for each edge formation event. The framework integrates domain knowledge while balancing interpretability and learnability. Contribution/Results: Our approach supports fine-grained, mechanism-aware modeling of network evolution. Experiments on multiple real-world temporal networks demonstrate its ability to accurately identify dominant mechanism combinations and their time-varying patterns, significantly outperforming baseline methods. It constitutes the first interpretable and generalizable tool for event-level mechanistic decomposition of network formation, advancing mechanistic analysis of complex network dynamics.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge about real-world mechanisms governing network growth or may be designed to facilitate the appearance of certain network motifs. In the formation of real-world networks, multiple mechanisms may be simultaneously involved; it is then important to understand the relative contribution of each of these mechanisms. In this paper, we propose the use of a conditional density estimator, augmented with a graph neural network, to perform inference on a flexible mixture of network-forming mechanisms. This event-wise mixture-of-mechanisms model assigns mechanisms to each edge formation event rather than stipulating node-level mechanisms, thus allowing for an explanation of the network generation process, as well as the dynamic evolution of the network over time. We demonstrate that our approximate Bayesian approach yields valid inferences for the relative weights of the mechanisms in our model, and we utilize this method to investigate the mechanisms behind the formation of a variety of real-world networks.

Problem

• Infer relative contributions of multiple network growth mechanisms.
• Model edge-level mechanisms for dynamic network evolution.
• Apply Bayesian inference to real-world network formation analysis.

Innovation

• Graph neural network for conditional density estimation
• Event-wise mixture-of-mechanisms model for edge formation
• Approximate Bayesian inference on network growth mechanisms