Efficient graphlet kernels for large graph comparison

By Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten M. Borgwardt. 2009

1,128 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Graph Learning

Citations in the corpus

Learning Convolutional Neural Networks for Graphs (2016, methodology): “We compared P ATCHY - SAN with the shortest-path kernel (SP) (Borgwardt & Kriegel, 2005), the random walk kernel (RW) (Gaertner et al., 2003), the graphlet count kernel (GK) (Shervashidze et al., 2009), and the Weisfeiler-Lehman subtree kernel (WL) (Shervashidze et al., 2011).”
Discriminative Embeddings of Latent Variable Models for Structured Data (2016, methodology): “oint is represented as a vector of counts for elementary structures. The spectrum kernel and variants for strings (Leslie et al., 2002a), subtree kernel (Ramon & Gartner, 2003), graphlet kernel (Shervashidze et al., 2009) and Weisfeiler-lehman graph kernel (Shervashidze et al., 2011) all follow ”
The More You Know: Using Knowledge Graphs for Image Classification (2016, background): “There is also a substantial amount of work on various types of kernels deﬁned for graphs [35] such as diffusion kernels [14], graphlet kernels [32], Weisfeiler-Lehman graph kernels [31], deep graph kernels [26], graph invariant kernels [25] and shortest-path kernels [1].”
Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, background): “Different graph kernels extract different types of substructures in a graph, e.g. graphlets (Shervashidze et al., 2009), subtree patterns (Shervashidze & Borgwardt, 2009), etc., and the resulting vector representation for each graph is typically called “feature vector” (Yanardag & Vishwanathan,…”
Pre-training Graph Neural Networks with Kernels (2018, neutral): “State-of-the-art machine learning techniques for classiﬁcation and regression on graphs are at the moment kernel machines equipped with speciﬁcally designed kernels for graphs (e.g, Shervashidze et al. [2009]; Vishwanathan et al. [2010]; Da San Martino et al. [2012a]).”
Hierarchical Graph Representation Learning with Differentiable Pooling (2018, methodology): “We use the GRAPHLET [35], the SHORTEST-PATH [2], WEISFEILERLEHMAN kernel (WL) [34], and WEISFEILER-LEHMAN OPTIMAL ASSIGNMENT KERNEL (WLOA) [23] as kernel baselines.”
An End-to-End Deep Learning Architecture for Graph Classification (2018, methodology): “Baselines and experimental setting We compare DGCNN with four graph kernels: the graphlet kernel (GK) (Shervashidze et al. 2009), the random walk kernel (RW) (Vishwanathan et al.”
Graphite: Iterative Generative Modeling of Graphs (2018, background): “Extensive prior work has led to the development of graph kernels that are either hand designed based on graph properties such as subgraph patterns (Shervashidze et al., 2009; Shervashidze and Borgwardt, 2009) and shortest paths (Borgwardt and Kriegel, 2005), or explicitly learned from data (Yanardag”
Provably Powerful Graph Networks (2019, neutral): “GK (Shervashidze et al., 2009) 81.”
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018, methodology): “Prominent examples of this trend include kernels based on graphlet counting (Shervashidze et al. 2009), and, most notably, the Weisfeiler-Lehman subtree kernel (Shervashidze et al. 2011) as well as its higher-order variants (Morris, Kersting, and Mutzel 2017).”
Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks (2017, methodology): “occurrences of significant subgraph patterns, has also been used [13], but is a computationally expensive process.”
Propagation kernels: efficient graph kernels from propagated information (2014, background): “2013), kernels based on limited-size subgraphs (Horváth et al. 2004; Shervashidze et al. 2009; Kriege and Mutzel 2012), kernels based on subtree patterns (Mahé and Vert 2009; Ramon and Gärtner 2003), and kernels based on structure propagation (Shervashidze et al.”
The Multiscale Laplacian Graph Kernel (2016, methodology): “…the Weisfeiler–Lehman Kernel, the Weisfeiler–Lehman Edge Kernel (Shervashidze et al., 2009), the Shortest Path Kernel (Borgwardt & Kriegel, 2005), the Graphlet Kernel (Shervashidze et al., 2009), and the p -random Walk Kernel (Vishwanathan et al., 2010), on standard benchmark datasets(Table 2).”
Capsule Graph Neural Network (2018, methodology): “, 2011), the graphlet count kernel(GK) (Shervashidze et al., 2009), and the Random Walk (RW) (Vishwanathan et al.”
Matching Node Embeddings for Graph Similarity (2017, methodology): “…that counts common walks in two graphs (Vishwanathan et al. 2010), (2) the graphlet kernel (GR) that counts common connected subgraphs of size 3 (Shervashidze et al. 2009), (3) the shortest path kernel (SP) that counts shortest paths of equal length between pairs of nodes (Borgwardt and Kriegel…”