Experimental Evaluation of Graph Classification with Hadamard Code Graph Kernels

Graph kernels are of vital importance in the field of graph comparison and classification. However, how to compare and evaluate graph kernels and how to choose an optimal kernel for a practical classification problem remain open problems. In this paper, a comprehensive evaluation framework of graph kernels is proposed for unattributed graph classification. According to the kernel design methods, the whole graph kernel family can be categorized in five different dimensions, and then several representative graph kernels are chosen from these categories to perform the evaluation. With plenty of real-world and synthetic datasets, kernels are compared by many criteria such as classification accuracy, F1 score, runtime cost, scalability and applicability. Finally, quantitative conclusions are discussed based on the analyses of the extensive experimental results. The main contribution of this paper is that a comprehensive evaluation framework of graph kernels is proposed, which is significant for graph-classification applications and the future kernel research.

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GRAPH CLASSIFICATION BASED ON VECTOR SPACE EMBEDDING

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800140900748x ◽

2009 ◽

Vol 23 (06) ◽

pp. 1053-1081 ◽

Cited By ~ 48

Author(s):

KASPAR RIESEN ◽

HORST BUNKE

Keyword(s):

Data Sets ◽

Object Representations ◽

Graph Classification ◽

Graph Edit Distance ◽

Graph Kernels ◽

Symbolic Data ◽

Representational Power ◽

High Degree ◽

Vectorial Data ◽

Representation Formalism

Graphs provide us with a powerful and flexible representation formalism for pattern classification. Many classification algorithms have been proposed in the literature. However, the vast majority of these algorithms rely on vectorial data descriptions and cannot directly be applied to graphs. Recently, a growing interest in graph kernel methods can be observed. Graph kernels aim at bridging the gap between the high representational power and flexibility of graphs and the large amount of algorithms available for object representations in terms of feature vectors. In the present paper, we propose an approach transforming graphs into n-dimensional real vectors by means of prototype selection and graph edit distance computation. This approach allows one to build graph kernels in a straightforward way. It is not only applicable to graphs, but also to other kind of symbolic data in conjunction with any kind of dissimilarity measure. Thus it is characterized by a high degree of flexibility. With several experimental results, we prove the robustness and flexibility of our new method and show that our approach outperforms other graph classification methods on several graph data sets of diverse nature.

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Graph Kernels: A Survey

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.13225 ◽

2021 ◽

Vol 72 ◽

pp. 943-1027

Author(s):

Giannis Nikolentzos ◽

Giannis Siglidis ◽

Michalis Vazirgiannis

Keyword(s):

Social Networks ◽

Comparative Study ◽

Experimental Evaluation ◽

Structured Data ◽

Research Activity ◽

Comprehensive Overview ◽

Graph Kernels ◽

The Past ◽

Considerable Research ◽

Wide Range

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.

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Graph Classification with Mapping Distance Graph Kernels

Lecture Notes in Computer Science - Pattern Recognition Applications and Methods ◽

10.1007/978-3-319-93647-5_2 ◽

2018 ◽

pp. 21-44

Author(s):

Tetsuya Kataoka ◽

Eimi Shiotsuki ◽

Akihiro Inokuchi

Keyword(s):

Distance Graph ◽

Graph Classification ◽

Graph Kernels

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A Degeneracy Framework for Graph Similarity

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/360 ◽

2018 ◽

Cited By ~ 10

Author(s):

Giannis Nikolentzos ◽

Polykarpos Meladianos ◽

Stratis Limnios ◽

Michalis Vazirgiannis

Keyword(s):

Time Complexity ◽

Global Perspective ◽

Graph Classification ◽

Graph Kernel ◽

Graph Kernels ◽

Graph Similarity ◽

The Core ◽

Global Properties ◽

Graph Comparison ◽

Benchmark Datasets

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Most existing methods for graph similarity focus either on local or on global properties of graphs. However, even if graphs seem very similar from a local or a global perspective, they may exhibit different structure at different scales. In this paper, we present a general framework for graph similarity which takes into account structure at multiple different scales. The proposed framework capitalizes on the well-known k-core decomposition of graphs in order to build a hierarchy of nested subgraphs. We apply the framework to derive variants of four graph kernels, namely graphlet kernel, shortest-path kernel, Weisfeiler-Lehman subtree kernel, and pyramid match graph kernel. The framework is not limited to graph kernels, but can be applied to any graph comparison algorithm. The proposed framework is evaluated on several benchmark datasets for graph classification. In most cases, the core-based kernels achieve significant improvements in terms of classification accuracy over the base kernels, while their time complexity remains very attractive.

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