mlsanityData to 7 Oct 2026

Residual2Vec: Debiasing graph embedding with random graphs

arXiv 2110.07654 ↗First posted 14 Oct 2021cs.LG cs.SI physics.soc-phcited by 11

Not on the map. Why?

Abstract (arXiv)

Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods hinge on a sampling of context nodes based on random walks. However, random walks can be a biased sampler due to the structural properties of graphs. Most notably, random walks are biased by the degree of each node, where a node is sampled proportionally to its degree. The implication of such biases has not been clear, particularly in the context of graph representation learning. Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs. We demonstrate that this debiasing not only improves link prediction and clustering performance but also allows us to explicitly model salient structural properties in graph embedding.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 77 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

24 of 77 references.

References · 24

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  8. 2017A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applicationscited by 260
  9. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 197
  10. 2015Censoring Representations with an Adversarycited by 191
  11. 2013Consistency of spectral clustering in stochastic block modelscited by 128
  12. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
12 more
  1. 2019DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classificationcited by 59
  2. 2019Understanding Undesirable Word Embedding Associationscited by 59
  3. 2018Towards Understanding Linear Word Analogiescited by 57
  4. 2016Random walks and diffusion on networkscited by 28
  5. 2014Notes on Noise Contrastive Estimation and Negative Samplingcited by 26
  6. 2021Frequency-based Distortions in Contextualized Word Embeddingscited by 19
  7. 2016Configuring Random Graph Models with Fixed Degree Sequencescited by 15
  8. 2016Variational Perspective on Local Graph Clusteringcited by 14
  9. 2014Identification of core-periphery structure in networkscited by 11
  10. 2017Learning Edge Representations via Low-Rank Asymmetric Projectionscited by 7
  11. 2019Statistical guarantees for local graph clusteringcited by 6
  12. 2017LASAGNE: Locality And Structure Aware Graph Node Embeddingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 3; 2025: 3; 2026: 1.

11 papers.

  1. 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
  2. 2023Toward Degree Bias in Embedding-Based Knowledge Graph Completioncited by 17
  3. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  4. 2021Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphscited by 11
  5. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 5
  6. 2024Robustness of shallow graph embedding methods for community detectioncited by 3
  7. 2024MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learningcited by 1
  8. 2025Learning Graph Node Embeddings by Smooth Pair Samplingcited by 0
  9. 2025FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Trainingcited by 0
  10. 2025Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attentioncited by 0
  11. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0