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Christos Giatsidis

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JMLR Journal 2020 Journal Article

GraKeL: A Graph Kernel Library in Python

Giannis Siglidis
Giannis Nikolentzos
Stratis Limnios
Christos Giatsidis
Konstantinos Skianis
Michalis Vazirgiannis

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/GraKeL. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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AAAI Conference 2014 Conference Paper

CoreCluster: A Degeneracy Based Graph Clustering Framework

Christos Giatsidis
Fragkiskos Malliaros
Dimitrios Thilikos
Michalis Vazirgiannis

Graph clustering or community detection constitutes an important task for investigating the internal structure of graphs, with a plethora of applications in several domains. Traditional tools for graph clustering, such as spectral methods, typically suffer from high time and space complexity. In this article, we present CORECLUSTER, an efficient graph clustering framework based on the concept of graph degeneracy, that can be used along with any known graph clustering algorithm. Our approach capitalizes on processing the graph in a hierarchical manner provided by its core expansion sequence, an ordered partition of the graph into different levels according to the k-core decomposition. Such a partition provides a way to process the graph in an incremental manner that preserves its clustering structure, while making the execution of the chosen clustering algorithm much faster due to the smaller size of the graph’s partitions onto which the algorithm operates.

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