A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation
CoRR(2024)
摘要
Many real-world datasets can be naturally represented as graphs, spanning a
wide range of domains. However, the increasing complexity and size of graph
datasets present significant challenges for analysis and computation. In
response, graph reduction techniques have gained prominence for simplifying
large graphs while preserving essential properties. In this survey, we aim to
provide a comprehensive understanding of graph reduction methods, including
graph sparsification, graph coarsening, and graph condensation. Specifically,
we establish a unified definition for these methods and introduce a
hierarchical taxonomy to categorize the challenges they address. Our survey
then systematically reviews the technical details of these methods and
emphasizes their practical applications across diverse scenarios. Furthermore,
we outline critical research directions to ensure the continued effectiveness
of graph reduction techniques, as well as provide a comprehensive paper list at
https://github.com/ChandlerBang/awesome-graph-reduction. We hope this survey
will bridge literature gaps and propel the advancement of this promising field.
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