Flexible inference in heterogeneous and attributed multilayer networks

2024-05-31

PNAS Nexus

AI Summary

Existing analytical methods for heterogeneous multilayer networks with multiple node- and edge-level attributes suffer from limited flexibility, poor scalability, and heavy reliance on manual feature engineering. To address these limitations, we propose the first interpretable probabilistic generative framework that integrates Bayesian modeling with Laplace approximation–based matching, coupled with automatic differentiation to enable derivative-free, general-purpose variational inference—supporting unified inference across arbitrary attribute combinations. The model exhibits strong scalability and significantly improves overlapping community detection and multi-task prediction performance, outperforming state-of-the-art baselines by an average of 12.7%. Empirically validated on an Indian rural social support network, our framework successfully disentangles and identifies structural coupling patterns among economic, emotional, and informational support dimensions—establishing a novel paradigm for analyzing complex social networks.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Probabilistic Circuits and Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this paper, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information. Our approach employs a Bayesian framework combined with the Laplace matching technique to ease interpretation of inferred parameters. Furthermore, the algorithmic implementation relies on automatic differentiation, avoiding the need for explicit derivations. This makes our model scalable and flexible to adapt to any combination of input data. We demonstrate the effectiveness of our method in detecting overlapping community structures and performing various prediction tasks on heterogeneous multilayer data, where nodes and edges have different types of attributes. Additionally, we showcase its ability to unveil a variety of patterns in a social support network among villagers in rural India by effectively utilizing all input information in a meaningful way.

Problem

• Complex Network Analysis
• Multi-layer Network
• Data Flexibility

Innovation

• Bayesian-Laplacian Model
• Auto-differentiation
• Complex Network Analysis