Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi
Abstract
Graph comparison is a fundamental operation in data mining and information retrieval. Due to the combinatorial nature of graphs, it is hard to balance the expressiveness of the similarity measure and its scalability. Spectral analysis provides quintessential tools for studying the multi-scale structure of graphs and is a well-suited foundation for reasoning about differences between graphs. However, computing full spectrum of large graphs is computationally prohibitive; thus, spectral graph comparison methods often rely on rough approximation techniques with weak error guarantees.
Topics & Concepts
ScalabilityComputer scienceTheoretical computer scienceSimilarity (geometry)Scale (ratio)GraphData miningArtificial intelligenceImage (mathematics)DatabaseQuantum mechanicsPhysicsRough Sets and Fuzzy LogicData Management and AlgorithmsGraph Theory and Algorithms