Fast Sequence Based Embedding with Diffusion Graphs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves properties such as distances between nodes. Vertex sequence based embedding procedures use features extracted from linear sequences of vertices to create embeddings using a neural network. In this paper, we propose diusion graphs as a method to rapidly generate vertex sequences for network embedding. Its computational eciency is superior to previous methods due to simpler sequence generation, and it produces more accurate results. In experiments, we found that the performance relative to other methods improves with increasing edge density in the graph. In a community detection task, clustering nodes in the embedding space produces better results compared to other sequence based embedding methods.
Original languageEnglish
Title of host publicationProceedings for International Conference on Complex Networks 2018
Place of PublicationBoston, USA
PublisherSpringer
Pages99-107
Number of pages12
ISBN (Electronic)978-3-319-73198-8
ISBN (Print)978-3-319-73197-1
DOIs
Publication statusPublished - 2018
EventInternational Conference on Complex Networks - Boston, United States
Duration: 5 Mar 20188 Mar 2018
https://complenet.weebly.com/

Publication series

NameSpringer Proceedings in Complexity (SPCOM)
PublisherSpringer, Cham
ISSN (Print)2213-8684
ISSN (Electronic)2213-8692

Conference

ConferenceInternational Conference on Complex Networks
Abbreviated titleCompleNet 2018
CountryUnited States
CityBoston
Period5/03/188/03/18
Internet address

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