NDLI: Approximate graph distance with imagisation

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Approximate graph distance with imagisation

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Approximate graph distance with imagisation

Content Provider	ACM Digital Library
Author	Hu, Bo
Abstract	Graph similarity can contribute to the solutions of a wide variety of real-life problems. Effective graph similarity measures, therefore, are in high demand in areas such as communication network, biology, medicine, finance, etc. Existing similarity methods present key shortcomings including high run-time computational cost and/or strong dependence on feature selection and feature engineering. Many of existing methods also suffer from ambiguity in the interpretation of resultant measurement. In this paper, we propose a fast approximation of graph similarity grounded on convolutional neural network based image embedding. Graph similarity (distance) is, therefore, translated into the quantitative comparison of the corresponding images faithfully encoding structural information of the graphs. The proposed method is validated with purposely built test data. In addition, we have also carried out a preliminary evaluation, that has demonstrated highly promising results: confirming the viability of proposed "imagisation" based graph distance measure.
Starting Page	115
Ending Page	124
Page Count	10
File Format	PDF
ISBN	9781450348072
DOI	10.1145/3011141.3011163
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Graph similarity Neural network Imagisation
Content Type	Text
Resource Type	Article

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