Add to ORKGData Mining and Machine Learning are in the midst of a \structured revolution" [1]. As we can\ud represent almost anything using graphs, learning and data mining on graphs have become a challenge\ud in various applications. The main algorithmic diculty in these areas, measuring similarity of graphs,\ud has therefore received signicant attention in recent past. Graph kernels proposes a theoretically\ud sound and promising approach to the problem of graph comparison. These kernels should respect\ud the information represented by the topology of the graphs, while being ecient to compute. Graph\ud kernel are used in elds like machine learning, data mining, language processing and bioinformatics.\ud Some of the existing graph kernel methods does...
Abstract Graph kernels have become an established and widely-used technique for solvi...
In recent years, graph kernels have received considerable interest within the machine learning and d...
Graph-structured data are becoming more and more abundant in many fields of science and engineering,...
Graph kernels are powerful tools to bridge the gap between machine learning and data encoded as grap...
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of ...
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of ...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In the real world all events are connected. There is a hidden network of dependencies that governs b...
The majority of popular graph kernels is based on the concept of Haussler's R-convolution kernel and...
We introduce propagation kernels, a general graph-kernel framework for eXciently measuring the simil...
Abstract Graph kernels have become an established and widely-used technique for solvi...
In recent years, graph kernels have received considerable interest within the machine learning and d...
Graph-structured data are becoming more and more abundant in many fields of science and engineering,...
Graph kernels are powerful tools to bridge the gap between machine learning and data encoded as grap...
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of ...
State-of-the-art graph kernels do not scale to large graphs with hundreds of nodes and thousands of ...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In this article, we propose a family of efficient kernels for large graphs with discrete node labels...
In the real world all events are connected. There is a hidden network of dependencies that governs b...
The majority of popular graph kernels is based on the concept of Haussler's R-convolution kernel and...
We introduce propagation kernels, a general graph-kernel framework for eXciently measuring the simil...
Abstract Graph kernels have become an established and widely-used technique for solvi...
In recent years, graph kernels have received considerable interest within the machine learning and d...
Graph-structured data are becoming more and more abundant in many fields of science and engineering,...