Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation
Graph augmentation methods play a crucial role in improving the performance and enhancing generalisation capabilities in Graph Neural Networks (GNNs). Existing graph augmentation methods mainly perturb the graph structures, and are usually limited to pairwise node relations. These methods cannot ful...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
20.02.2024
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Subjects | |
Online Access | Get full text |
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Summary: | Graph augmentation methods play a crucial role in improving the performance
and enhancing generalisation capabilities in Graph Neural Networks (GNNs).
Existing graph augmentation methods mainly perturb the graph structures, and
are usually limited to pairwise node relations. These methods cannot fully
address the complexities of real-world large-scale networks, which often
involve higher-order node relations beyond only being pairwise. Meanwhile,
real-world graph datasets are predominantly modelled as simple graphs, due to
the scarcity of data that can be used to form higher-order edges. Therefore,
reconfiguring the higher-order edges as an integration into graph augmentation
strategies lights up a promising research path to address the aforementioned
issues. In this paper, we present Topological Augmentation (TopoAug), a novel
graph augmentation method that builds a combinatorial complex from the original
graph by constructing virtual hyperedges directly from the raw data. TopoAug
then produces auxiliary node features by extracting information from the
combinatorial complex, which are used for enhancing GNN performances on
downstream tasks. We design three diverse virtual hyperedge construction
strategies to accompany the construction of combinatorial complexes: (1) via
graph statistics, (2) from multiple data perspectives, and (3) utilising
multi-modality. Furthermore, to facilitate TopoAug evaluation, we provide 23
novel real-world graph datasets across various domains including social media,
biology, and e-commerce. Our empirical study shows that TopoAug consistently
and significantly outperforms GNN baselines and other graph augmentation
methods, across a variety of application contexts, which clearly indicates that
it can effectively incorporate higher-order node relations into the graph
augmentation for real-world complex networks. |
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DOI: | 10.48550/arxiv.2402.13033 |