Neural Subgraph Counting with Wasserstein Estimator
Hanchen Wang, Rong Hu, Ying Zhang, Lu Qin, Wei Wang, Wenjie Zhang
Abstract
Subgraph counting is a fundamental graph analysis task which has been widely used in many applications. As the problem of subgraph counting is NP-complete and hence intractable, approximate solutions have been widely studied, which fail to work with large and complex query graphs. Alternatively, Machine Learning techniques have been recently applied for this problem, yet the existing ML approaches either only support very small data graphs or cannot make full use of the data graph information, which inherently limits their scalability, estimation accuracies and robustness.
Topics & Concepts
Computer scienceScalabilityRobustness (evolution)EstimatorGraphInduced subgraph isomorphism problemTheoretical computer scienceBig dataArtificial intelligenceAlgorithmData miningMathematicsLine graphChemistryBiochemistryGeneStatisticsVoltage graphDatabaseGraph Theory and AlgorithmsAdvanced Graph Neural NetworksTopological and Geometric Data Analysis