Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
CoRR(2023)
摘要
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/.
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关键词
unified graph representation,complex graphs,datasets,benchmarks
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