Graph Invariant Kernels

By Francesco Orsini, Paolo Frasconi, Luc De Raedt. 2015

136 citations recorded by the source corpus.

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In Graph Learning

Citations in the corpus

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 [36] such as diffusion kernels [14], graphlet kernels [33], Weisfeiler-Lehman graph kernels [32], deep graph kernels [27], graph invariant kernels [26] and shortest-path kernels [1].”
Pre-training Graph Neural Networks with Kernels (2018, neutral): “More recent frameworks for graph kernels addressing efﬁciency or efﬁcacy have been proposed in Neumann et al. [2015]; Orsini et al. [2015].”
A survey on graph kernels (2019, methodology): “Graph Invariant [33] 2015”
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (2019, background): “Another common approach to unsupervised representation learning on graphs is through graph kernels [48, 21, 43].”
Tree-Based Kernel for Graphs With Continuous Attributes (2015, methodology): “In Table I we report the experimental results of the proposed ODDCL ST kernel, its approximated version ODDCL Approx presented in Section V-C, the Propagation kernel ( P 2 K ) [11], different instantiations of the Graph Invariant Kernels [12] (GIK NSPDK, GIK SGK GLOBAL, GIK SGK LOCAL, GIK WL GLOBAL,”
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (2017, background): “kernel [9], Weisfeiler-Lehman kernel [31], deep graph kernels [34], graph invariant kernels [27] and multiscale Laplacian graph kernel [21].”
RetGK: Graph Kernels based on Return Probabilities of Random Walks (2018, methodology): “We compare our graph kernels with many state-of-the-art graph classification algorithms: (i) the shortest path kernel (SP) [1], (ii) the Weisfeiler-Lehman subtree kernel (WL) [25], (iii) the graphlet count kernel (GK)[26], (iv) deep graph kernels (DGK) [31], (v) PATCHY-SAN convolutional neural netwo”
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (2019, background): “FRANKENSTEIN (Orsini, Frasconi, and De Raedt 2015) contains molecules as graph for mutagen classiﬁcation.”
Self-Attention Graph Pooling (2019, background): “Since the global pooling architecture POOL g (SAGPool g , SortPool g , Set2Set g ) minimizes the loss of information, it performs better than the hierarchical pooling architecture POOL h (SAGPool h , gPool h , DiffPool h ) on datasets with fewer nodes (NCI1, NCI109, FRANKENSTEIN).”
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (2017, unknown): “, see [1]–[4].”
Graph Capsule Convolutional Neural Networks (2018, background): “Furthermore, deep graph kernels ( Yanardag & Vishwanathan, 2015), graph invariant kernels (Orsini et al., 2015), optimal assignment graph kernels (Kriege et al., 2016) and multiscale laplacian graph kernel (Kondor & Pan, 2016) have been proposed with the goal to re-deﬁne kernel functions to…”