research-article

Random-Walk Graph Embeddings and the Influence of Edge Weighting Strategies in Community Detection Tasks

Authors:

Andreas Kosmatopoulos,

Kostas Loumponias,

Despoina Chatzakou,

Theodora Tsikrika,

Stefanos Vrochidis,

Ioannis KompatsiarisAuthors Info & Claims

OASIS '21: Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks

Pages 9 - 13

https://doi.org/10.1145/3472720.3483621

Published: 28 October 2021 Publication History

Abstract

Graph embedding methods have been developed over recent years with the goal of mapping graph data structures into low dimensional vector spaces so that conventional machine learning tasks can be efficiently evaluated. In particular, random walk based methods sample the graph using random walk sequences that capture a graph's structural properties. In this work, we study the influence of edge weighting strategies that bias the random walk process and we are able to demonstrate that under several settings the biased random walks enhance downstream community detection tasks.

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Cited By

Loumponias KKosmatopoulos ATsikrika TVrochidis SKompatsiaris I(2023)Modified SkipGram Negative Sampling Model for Faster Convergence of Graph EmbeddingDeep Learning Theory and Applications10.1007/978-3-031-37317-6_1(1-16)Online publication date: 7-Jul-2023
https://doi.org/10.1007/978-3-031-37317-6_1

Index Terms

Random-Walk Graph Embeddings and the Influence of Edge Weighting Strategies in Community Detection Tasks
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Networks
  1. Network types
    1. Overlay and other logical network structures
      1. Online social networks

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cover image ACM Conferences

OASIS '21: Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks

October 2021

44 pages

ISBN:9781450386326

DOI:10.1145/3472720

General Chairs:
Barbara Guidi
University of Pisa, Italy
,
Andrea Michienzi
University of Pisa, Italy
,
Laura Ricci
University of Pisa, Italy

Copyright © 2021 ACM.

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Publication History

Published: 28 October 2021

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European Commission

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HT '21

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HT '21: 32nd ACM Conference on Hypertext and Social Media

August 30, 2021

Virtual Event, Ireland

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Cited By

Loumponias KKosmatopoulos ATsikrika TVrochidis SKompatsiaris I(2023)Modified SkipGram Negative Sampling Model for Faster Convergence of Graph EmbeddingDeep Learning Theory and Applications10.1007/978-3-031-37317-6_1(1-16)Online publication date: 7-Jul-2023
https://doi.org/10.1007/978-3-031-37317-6_1

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