mlsanityData to 7 Oct 2026

DeepWalk: Online Learning of Social Representations

arXiv 1403.6652 ↗First posted 26 Mar 2014cs.SI cs.LGcited by 1,543

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Abstract (arXiv)

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.

Key facts. Cited by 1,543 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 409; 2022: 340; 2023: 292; 2024: 237; 2025: 168; 2026: 97.

1,543 papers.

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  12. 2021Motif-based Graph Self-Supervised Learning for Molecular Property Predictioncited by 159
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  25. 2021Graph Representation Learning in Biomedicinecited by 79
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  28. 2022Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddingscited by 75
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  30. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
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