mlsanityData to 7 Oct 2026

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

arXiv 2302.00873 ↗First posted 2 Feb 2023cs.LG cs.SIcited by 10

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Abstract (arXiv)

Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary people (silent) on the twitter's social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing message-passing based GNNs assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open sourced.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 43 of its 88 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

43 of 88 references.

References · 43

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31 more
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Cited by year

Citing papers per year: 2023: 6; 2024: 4.

10 papers.

  1. 2023Energy-based Out-of-Distribution Detection for Graph Neural Networkscited by 67
  2. 2024A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptationcited by 16
  3. 2023GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networkscited by 12
  4. 2023Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfercited by 10
  5. 2023On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filteringcited by 9
  6. 2024Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphscited by 6
  7. 2024Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learningcited by 2
  8. 2024LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNNcited by 2
  9. 2023UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimizationcited by 1
  10. 2023HKTGNN: Hierarchical Knowledge Transferable Graph Neural Network-based Supply Chain Risk Assessmentcited by 0