Cyclic pattern kernels for predictive graph mining

By Tamás Horváth, Thomas Gärtner, Stefan Wrobel. 2004

377 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs (2016, background): “…//use Algorithm 2 3 Initialization: Sample Φ from U |SGV ocab|×δ 4 for e = 0 to e do 5 G = Shuffle (G) 6 for each Gi ∈ G do 7 for each v ∈ Vi do 8 for d = 0 to D do 9 sg (d) v := GetWLSubgraph(v,Gi, d) 10 RadialSkipGram (Φ, sg (d) v , Gi, D) 11 return Φ revealing that the learned…”
Graph Invariant Kernels (2015, methodology): “…patterns [Ramon and Gärtner, 2003; Mahé and Vert, 2009], paths [Kashima et al., 2003] or shortest paths [Borgwardt and Kriegel, 2005], cyclic and tree patterns [Horváth et al., 2004], neighborhoods [Costa and De Grave, 2010], or arbitrary frequent patterns [Deshpande et al., 2005].”
Fast subtree kernels on graphs (2009, background): “Several different graph kernels have been defined in machine learning which can be categorized into three classes: graph kernels based on walks [5, 7] and paths [2], graph kernels based on limited-size subgraphs [6, 11], and graph kernels based on subtree patterns [9, 10].”
A Degeneracy Framework for Graph Similarity (2018, neutral): “…[G¨artner et al. , 2003; Vishwanathan et al. , 2010; Sugiyama and Borgwardt, 2015], subtrees [G¨artner et al. , 2003; Mah´e and Vert, 2009], cycles [Horv´ath et al. , 2004], shortest paths [Borgwardt and Kriegel, 2005], and small sub-graphs [Shervashidze et al. , 2009; Kriege and Mutzel, 2012].”
Protein-ligand interaction prediction: an improved chemogenomics approach (2008, methodology): “Other kernels have been designed directly from the comparison of 2D and 3D structures of molecules, including kernels based on the detection of common substructures in the 2D structures molecules seen as graphs (Kashima et al., 2003, 2004; Gärtner et al., 2003; Mahé et al., 2005; Ralaivolaet al., 20”
Efficient Graph Kernels by Randomization (2012, background): “Graph kernels can be categorized mainly into four classes: graph kernels based on walks [4, 10, 22] and paths [2], graph kernels based on limitedsize subgraphs [7,19], graph kernels based on subtree patterns [13,16], and graph”
Efficient graphlet kernels for large graph comparison (2009, background): “Many different graph kernels have been defined, which focus on different types of substructures in graphs, such as random walks (Gärtner et al., 2003; Kashima et al., 2004), shortest paths (Borgwardt & Kriegel, 2005), subtrees (Ramon & Gärtner, 2003), and cycles (Horvath et al., 2004).”
A survey on graph kernels (2019, methodology): “Molecular graphs are not only a common benchmark dataset for graph kernels, but several kernels were specifically proposed for this domain, e.g., (Horváth et al. 2004; Swamidass et al. 2005; Ceroni et al. 2007; Mahé and Vert 2009; Fröhlich et al. 2005).”
Graph Matching Networks for Learning the Similarity of Graph Structured Objects (2019, background): “…or paths on graphs (Borgwardt & Kriegel, 2005; Kashima et al., 2003; Vish-wanathan et al., 2010), kernels based on limited-sized sub-structures (Horváth et al., 2004; Shervashidze et al., 2009) and kernels based on sub-tree structures (Shervashidze & Borgwardt, 2009; Shervashidze et al., 2011).”
Deep Graph Kernels (2015, methodology): “Then, we discuss three major graph kernel families, namely, graph kernels based on limited-sized subgraphs [13, 28], graph kernels based on subtree patterns [24, 27], and graph kernels based on walks [14, 33] and paths [3].”
Graph kernels based on tree patterns for molecules (2006, methodology): “Horvath et al. (2004) introduced kernels based on the detection of common cyclic patterns, Menchetti et al. (2005) derived a kernel from the comparison of neighborhoods of atoms, and Ramon and Gartner (2003) introduced a kernel comparing graphs on the basis of their common subtrees.”
Detecting Similar Programs via The Weisfeiler-Leman Graph Kernel (2016, background): “Said kernel captures similar information to other kernels that count subgraphs (see for example [9] or [28]).”
Matching Node Embeddings for Graph Similarity (2017, background): “…kernels based on random walks (Kashima, Tsuda, and Inokuchi 2003; Gärtner, Flach, and Wrobel 2003), shortest paths (Borgwardt and Kriegel 2005), subtrees (Gärtner, Flach, and Wrobel 2003; Mahé and Vert 2009), cycles (Horváth, Gärtner, and Wrobel 2004) and graphlets (Shervashidze et al. 2009).”
Graph kernels for chemical informatics (2005, background): “Recently, Horváth et al. [2004] have proposed cyclic pattern kernels, derived by counting common occurrences of cycles and trees between two graphs.”
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (2017, background): “Prominent examples are kernels based on subgraphs up to a fixed size, so called graphlets [1], [33], or specific subgraphs like cycles and trees [34].”