Structure and inference in hypergraphs with node attributes

TL;DR

This paper introduces a model that integrates node attributes and higher-order interactions in hypergraphs to improve community detection and hyperedge prediction, automatically adjusting the influence of attributes based on their informativeness.

Contribution

The authors develop a scalable, principled method that combines node attributes with hypergraph structure for enhanced community detection and hyperedge prediction, adapting to the relevance of attributes.

Findings

Improves community detection accuracy when node attributes are informative.

Effectively predicts hyperedges in large hypergraphs.

Automatically weighs the contribution of node attributes based on their relevance.

Abstract

Many networked datasets with units interacting in groups of two or more, encoded with hypergraphs, are accompanied by extra information about nodes, such as the role of an individual in a workplace. Here we show how these node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We consider the problem of community detection in hypergraphs and develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions and to detect communities more accurately than using either of these types of information alone. The method learns automatically from the input data the extent to which structure and attributes contribute to explain the data, down weighing or discarding attributes if not informative. Our algorithmic implementation is efficient and scales to large hypergraphs and interactions of large numbers of units. We apply our method to a variety of systems, showing strong performance in hyperedge prediction tasks and in selecting community divisions that correlate with attributes when these are informative, but discarding them otherwise. Our approach illustrates the advantage of using informative node attributes when available with higher-order data.