The Multiscale Laplacian Graph Kernel
원문 초록
Many real world graphs, such as the graphs of molecules, exhibit structure at multiple different scales, but most existing kernels between graphs are either purely local or purely global in character. In contrast, by building a hierarchy of nested subgraphs, the Multiscale Laplacian Graph kernels (MLG kernels) that we define in this paper can account for structure at a range of different scales. At the heart of the MLG construction is another new graph kernel, called the Feature Space Laplacian Graph kernel (FLG kernel), which has the property that it can lift a base kernel defined on the vertices of two graphs to a kernel between the graphs. The MLG kernel applies such FLG kernels to subgraphs recursively. To make the MLG kernel computationally feasible, we also introduce a randomized projection procedure, similar to the Nystr\"om method, but for RKHS operators.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Density-aware Walks for Coordinated Campaign Detection
Positional Encoding meets Persistent Homology on Graphs
Improving the Effective Receptive Field of Message-Passing Neural Networks
Graph Style Transfer for Counterfactual Explainability
Scalable Graph Generative Modeling via Substructure Sequences
Addressing the Scarcity of Benchmarks for Graph XAI
토론
이 논문과 연결된 포럼 글이 아직 없습니다.