Machine Learning on Graphs with Kernels

Graphs are becoming a dominant structure in current information management with many domains involved, including social networks, chemistry, biology, etc. Many real-world problems require applying machine learning tasks to graph-structured data. Graph kernels have emerged as a promising approach for dealing with these tasks. A graph kernel is a symmetric, positive semidefinite function on the set of graphs. These functions extend the applicability of kernel methods to graphs. Graph kernels have attracted a lot of attention during the last 20 years. The considerable research activity that occurred in the field resulted in the development of dozens of kernels, each focusing on specific structural properties of graphs. The goal of this tutorial is to offer a comprehensive presentation of a wide range of graph kernels, and to describe their key applications. The tutorial will also offer to the participants hands-on experience in applying graph kernels to classification problems.

[1]  Jan Ramon,et al.  Expressivity versus efficiency of graph kernels , 2003 .

[2]  Marleen de Bruijne,et al.  Scalable kernels for graphs with continuous attributes , 2013, NIPS.

[3]  Thomas Gärtner,et al.  On Graph Kernels: Hardness Results and Efficient Alternatives , 2003, COLT.

[4]  Michalis Vazirgiannis,et al.  A Degeneracy Framework for Graph Similarity , 2018, IJCAI.

[5]  Michalis Vazirgiannis,et al.  Enhancing Graph Kernels via Successive Embeddings , 2018, CIKM.

[6]  Thomas Gärtner,et al.  Cyclic pattern kernels for predictive graph mining , 2004, KDD.

[7]  Luc De Raedt,et al.  Graph Invariant Kernels , 2015, IJCAI.

[8]  Devdatt P. Dubhashi,et al.  Global graph kernels using geometric embeddings , 2014, ICML.

[9]  Michalis Vazirgiannis,et al.  Matching Node Embeddings for Graph Similarity , 2017, AAAI.

[10]  Michalis Vazirgiannis,et al.  GraKeL: A Graph Kernel Library in Python , 2018, J. Mach. Learn. Res..

[11]  Alessandro Sperduti,et al.  A Tree-Based Kernel for Graphs , 2012, SDM.

[12]  Nils M. Kriege,et al.  On Valid Optimal Assignment Kernels and Applications to Graph Classification , 2016, NIPS.

[13]  Hisashi Kashima,et al.  Marginalized Kernels Between Labeled Graphs , 2003, ICML.

[14]  David Haussler,et al.  Convolution kernels on discrete structures , 1999 .

[15]  Kurt Mehlhorn,et al.  Efficient graphlet kernels for large graph comparison , 2009, AISTATS.

[16]  Hans-Peter Kriegel,et al.  Shortest-path kernels on graphs , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[17]  Roman Garnett,et al.  Propagation kernels: efficient graph kernels from propagated information , 2015, Machine Learning.

[18]  Kurt Mehlhorn,et al.  Weisfeiler-Lehman Graph Kernels , 2011, J. Mach. Learn. Res..

[19]  Pinar Yanardag,et al.  Deep Graph Kernels , 2015, KDD.

[20]  S. V. N. Vishwanathan,et al.  A Structural Smoothing Framework For Robust Graph Comparison , 2015, NIPS.