# A Degeneracy Framework for Graph Similarity

@inproceedings{Nikolentzos2018ADF, title={A Degeneracy Framework for Graph Similarity}, author={Giannis Nikolentzos and Polykarpos Meladianos and Stratis Limnios and Michalis Vazirgiannis}, booktitle={IJCAI}, year={2018} }

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…

## 46 Citations

A survey on graph kernels

- Computer ScienceAppl. Netw. Sci.
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This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years and describes and categorizes graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice.

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- Computer ScienceBig Data
- 2019

This paper develops a novel graph similarity measure that overcomes the above limitations and identifies similar graphs and extends to large-scale graphs.

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- 2021

This work proposes a family of graph kernels that incorporate existence intervals of features and empirically validate the expressive power of the graph kernels and show significant improvements over state-of-the-art graph kernels in terms of predictive performance on various real-world benchmark datasets.

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- Computer ScienceArXiv
- 2018

This work proposes a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance, and suggests SimGNN provides a new direction for future research on graph similarity computation and graph similarity search.

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- Computer ScienceWSDM
- 2019

This work proposes a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance, and suggests SimGNN provides a new direction for future research on graph similarity computation and graph similarity search.

The core decomposition of networks: theory, algorithms and applications

- Computer ScienceThe VLDB Journal
- 2019

In this survey, an in-depth discussion of core decomposition is performed, focusing mainly on the basic theory and fundamental concepts, the algorithmic techniques proposed for computing it efficiently under different settings, and the applications that can benefit significantly from it.

Machine Learning on Graphs with Kernels

- Computer ScienceCIKM
- 2019

This tutorial is to offer a comprehensive presentation of a wide range of graph kernels, and to describe their key applications, and will also offer to the participants hands-on experience in applying graph kernels to classification problems.

Graph convolutional networks with multi-level coarsening for graph classification

- Computer Science, MathematicsKnowl. Based Syst.
- 2020

Using Laplacian Spectrum as Graph Feature Representation

- Computer ScienceArXiv
- 2019

This work first reminds and shows that GLS satisfies the aforementioned key attributes, using a graph perturbation approach, and derives bounds for the distance between two GLS that are related to the \textit{divergence to isomorphism}, a standard computationally expensive graph divergence.

About Graph Degeneracy, Representation Learning and Scalability

- Computer ScienceArXiv
- 2020

This paper presents two techniques taking advantage of a degeneracy property of Graphs - the K-Core Decomposition to reduce the time and memory consumption of walk-based Graph Representation Learning algorithms.

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