Hypergraphs with node attributes: structure and inference
- URL: http://arxiv.org/abs/2311.03857v1
- Date: Tue, 7 Nov 2023 10:16:20 GMT
- Title: Hypergraphs with node attributes: structure and inference
- Authors: Anna Badalyan, Nicol\`o Ruggeri and Caterina De Bacco
- Abstract summary: We show how node attributes can be used to improve our understanding of the structure resulting from higher-order interactions.
We develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
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