Evaluating random walk-based network embeddings for Web service applications

Olayinka Adeleye*, Jian Yu, Ji Ruan, Quan Z. Sheng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


Network embedding models automatically learn low-dimensional and neighborhood graph representation in vector space. Even-though these models have shown improved performances in various applications such as link prediction and classification compare to traditional graph mining approaches, they are still difficult to interpret. Most works rely on visualization for the interpretation. Moreover, it is challenging to quantify how well these models can preserve the topological properties of real networks such as clustering, degree centrality and betweenness. In this paper, we study the performance of recent unsupervised network embedding models in Web service application. Specifically, we investigate and analyze the performance of recent random walk-based embedding approaches including node2vec, DeepWalk, LINE and HARP in capturing the properties of Web service networks and compare the performances of the models for basic web service prediction tasks. We based the study on the Web service networks constructed in our previous works. We evaluate the models with respect to the precision with which they unpack specific topological properties of the networks. We investigate the influence of each topological property on the accuracy of the prediction task. We conduct our experiment using the popular ProgrammableWeb dataset. The results present in this work are expected to provide insight into application of network embedding in service computing domain especially for applications that aim at exploiting machine learning models.

Original languageEnglish
Title of host publicationDatabases Theory and Applications
Subtitle of host publication31st Australasian Database Conference, ADC 2020, Proceedings
EditorsRenata Borovica-Gajic, Jianzhong Qi, Weiqing Wang
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Number of pages8
ISBN (Electronic)9783030394691
ISBN (Print)9783030394684
Publication statusPublished - 2020
Event31st Australasian Database Conference, ADC 2019 - Melbourne, Australia
Duration: 3 Feb 20207 Feb 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12008 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference31st Australasian Database Conference, ADC 2019


  • Embedding
  • Link prediction
  • Web service network


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