17 research outputs found
From Hypergraph Energy Functions to Hypergraph Neural Networks
Hypergraphs are a powerful abstraction for representing higher-order
interactions between entities of interest. To exploit these relationships in
making downstream predictions, a variety of hypergraph neural network
architectures have recently been proposed, in large part building upon
precursors from the more traditional graph neural network (GNN) literature.
Somewhat differently, in this paper we begin by presenting an expressive family
of parameterized, hypergraph-regularized energy functions. We then demonstrate
how minimizers of these energies effectively serve as node embeddings that,
when paired with a parameterized classifier, can be trained end-to-end via a
supervised bilevel optimization process. Later, we draw parallels between the
implicit architecture of the predictive models emerging from the proposed
bilevel hypergraph optimization, and existing GNN architectures in common use.
Empirically, we demonstrate state-of-the-art results on various hypergraph node
classification benchmarks. Code is available at
https://github.com/yxzwang/PhenomNN.Comment: Accepted to ICML 202
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Graphs are widely used to encapsulate a variety of data formats, but
real-world networks often involve complex node relations beyond only being
pairwise. While hypergraphs and hierarchical graphs have been developed and
employed to account for the complex node relations, they cannot fully represent
these complexities in practice. Additionally, though many Graph Neural Networks
(GNNs) have been proposed for representation learning on higher-order graphs,
they are usually only evaluated on simple graph datasets. Therefore, there is a
need for a unified modelling of higher-order graphs, and a collection of
comprehensive datasets with an accessible evaluation framework to fully
understand the performance of these algorithms on complex graphs. In this
paper, we introduce the concept of hybrid graphs, a unified definition for
higher-order graphs, and present the Hybrid Graph Benchmark (HGB). HGB contains
23 real-world hybrid graph datasets across various domains such as biology,
social media, and e-commerce. Furthermore, we provide an extensible evaluation
framework and a supporting codebase to facilitate the training and evaluation
of GNNs on HGB. Our empirical study of existing GNNs on HGB reveals various
research opportunities and gaps, including (1) evaluating the actual
performance improvement of hypergraph GNNs over simple graph GNNs; (2)
comparing the impact of different sampling strategies on hybrid graph learning
methods; and (3) exploring ways to integrate simple graph and hypergraph
information. We make our source code and full datasets publicly available at
https://zehui127.github.io/hybrid-graph-benchmark/.Comment: Preprint. Under review. 16 pages, 5 figures, 11 table