scientific article; zbMATH DE number 6253950
From MaRDI portal
Publication:5396694
zbMath1280.68194MaRDI QIDQ5396694
Karsten M. Borgwardt, Pascal Schweitzer, Nino Shervashidze, Erik Jan van Leeuwen, Kurt Mehlhorn
Publication date: 3 February 2014
Full work available at URL: http://www.jmlr.org/papers/v12/shervashidze11a.html
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of graph theory (05C90) Learning and adaptive systems in artificial intelligence (68T05)
Related Items
GTED: Graph Traversal Edit Distance, Binary vectors for fast distance and similarity estimation, Effectiveness of Representation and Length Variation of Shortest Paths in Graph Classification, Propagation kernels: efficient graph kernels from propagated information, Unnamed Item, Polynomial-based graph convolutional neural networks for graph classification, Unnamed Item, Synchronization Over the Birkhoff Polytope for Multi-graph Matching, Finding the best not the most: regularized loss minimization subgraph selection for graph classification, Lov\'asz Meets Weisfeiler and Leman, Efficient subgraph matching using topological node feature constraints, Graph isomorphism, color refinement, and compactness, On the Power of Color Refinement, Locality preserving dense graph convolutional networks with graph context-aware node representations, Neural predictor-based automated graph classifier framework, A new measure for the attitude to mobility of Italian students and graduates: a topological data analysis approach, The resistance perturbation distance: a metric for the analysis of dynamic networks, An expressive dissimilarity measure for relational clustering using neighbourhood trees, kProbLog: an algebraic Prolog for machine learning, Classification on Large Networks: A Quantitative Bound via Motifs and Graphons (Research), The journey of graph kernels through two decades, Counts-of-counts similarity for prediction and search in relational data, A unifying view of explicit and implicit feature maps of graph kernels, kLog: a language for logical and relational learning with kernels, A Mixed Weisfeiler-Lehman Graph Kernel, A Quantum Jensen-Shannon Graph Kernel Using Discrete-Time Quantum Walks, Upper Bounds on the Quantifier Depth for Graph Differentiation in First Order Logic, On Whole-Graph Embedding Techniques, K-plex cover pooling for graph neural networks, Unnamed Item, Interpretable multi-scale graph descriptors via structural compression, Unnamed Item, Unnamed Item, A combined Weisfeiler–Lehman graph kernel for structured data, Two-sample Hypothesis Testing for Inhomogeneous Random Graphs, A quotient space formulation for generative statistical analysis of graphical data, On Weisfeiler-Leman invariance: subgraph counts and related graph properties, The analysis from nonlinear distance metric to kernel-based prescription prediction system, Story embedding: learning distributed representations of stories based on character networks, A gentle introduction to deep learning for graphs, \(k\)-hop graph neural networks, The Complexity of Homomorphism Indistinguishability, kProbLog: An Algebraic Prolog for Kernel Programming, Distance metric learning for graph structured data, \texttt{OWL2Vec}*: embedding of OWL ontologies, Attentional multilabel learning over graphs: a message passing approach, Unnamed Item, Some Ulam’s reconstruction problems for quantum states, A generalized Weisfeiler-Lehman graph kernel, Sharp local minimax rates for goodness-of-fit testing in multivariate binomial and Poisson families and in multinomials, Graph Kernels: A Survey