Learning over Families of Sets – Hypergraph Representation Learning for Higher Order Tasks

Graph representation learning has made major strides over the past decade. However, in many relational domains, the input data are not suited for simple graph representations as the relationships between entities go beyond pairwise interactions. In such cases, the relationships in the data are better represented as hyperedges (set of entities) of a non-uniform hypergraph. While there have been works on principled methods for learning representations of nodes of a hypergraph, these approaches are limited in their applicability to tasks on non-uniform hypergraphs (hyperedges with different cardinalities). In this work, we exploit the incidence structure to develop a hypergraph neural network to learn provably expressive representations of variable sized hyperedges which preserve local-isomorphism in the line graph of the hypergraph, while also being invariant to permutations of its constituent vertices. Specifically, for a given vertex set, we propose frameworks for (1) hyperedge classification and (2) variable sized expansion of partially observed hyperedges which captures the higher order interactions among vertices and hyperedges. We evaluate performance on multiple real-world hypergraph datasets and demonstrate consistent, significant improvement in accuracy, over state-of-the-art models.


page 1

page 2

page 3

page 4


Hypergraph Learning with Line Expansion

Previous hypergraph expansions are solely carried out on either vertex l...

Community Detection in General Hypergraph via Graph Embedding

Network data has attracted tremendous attention in recent years, and mos...

Efficient Computation of High-Order Line Graphs of Hypergraphs

This paper considers structures of systems beyond dyadic (pairwise) inte...

Recurrently Predicting Hypergraphs

This work considers predicting the relational structure of a hypergraph ...

High-order Line Graphs of Non-uniform Hypergraphs: Algorithms, Applications, and Experimental Analysis

Hypergraphs offer flexible and robust data representations for many appl...

Learning Non-Uniform Hypergraph for Multi-Object Tracking

The majority of Multi-Object Tracking (MOT) algorithms based on the trac...

IHGNN: Interactive Hypergraph Neural Network for Personalized Product Search

A good personalized product search (PPS) system should not only focus on...

Please sign up or login with your details

Forgot password? Click here to reset