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Database Performance Tuning and Query Optimization Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Comparative Study of Network Embedding Based on Matrix Factorization

Authors : Xin Liu, Kyoung-Sook Kim

Published in: Data Mining and Big Data

Publisher: Springer International Publishing

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Abstract

In the era of big data, the study of networks has received an enormous amount of attention. Of recent interest is network embedding—learning representations of the nodes of a network in a low dimensional vector space, so that the network structural information and properties are maximally preserved. In this paper, we present a review of the latest developments on this topic. We compare modern methods based on matrix factorization, including GraRep [5], HOPE [22], DeepWalk [23], and node2vec [12], in a collection of 12 real-world networks. We find that the performance of methods depends on the applications and the specific characteristics of the networks. There is no clear winner for all of the applications and in all of the networks. In particular, node2vec exhibits relatively reliable performance in the multi-label classification application, while HOPE demonstrates success in the link prediction application. Moreover, we provide suggestions on how to choose a method for practical purposes in terms of accuracy, speed, stability, and prior knowledge requirement.

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previous chapter Human Occupation as a Complex System
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10
The similar reason also applies to the Cora and Citeseer networks.
 
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Metadata
Title
A Comparative Study of Network Embedding Based on Matrix Factorization
Authors
Xin Liu
Kyoung-Sook Kim
Copyright Year
2018
Book
Data Mining and Big Data

Print ISBN: 978-3-319-93802-8

Electronic ISBN: 978-3-319-93803-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-93803-5_9

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