research-article

DeepWalk: online learning of social representations

Authors:

Steven SkienaAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 701 - 710

https://doi.org/10.1145/2623330.2623732

Published: 24 August 2014 Publication History

Abstract

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs.

DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data.

DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.

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Index Terms

DeepWalk: online learning of social representations
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

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Sofus Macskassy
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Program Chairs:
Jure Leskovec
Stanford University
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Wei Wang
UCLA
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Rayid Ghani
University of Chicago

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Published: 24 August 2014

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https://doi.org/10.7717/peerj-cs.2648
Zhao ZZhu MWang CWang SZhang JChen LCai W(2025)Multiplex Graph Contrastive Learning with Soft NegativesElectronics10.3390/electronics1402039614:2(396)Online publication date: 20-Jan-2025
https://doi.org/10.3390/electronics14020396
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