Research map / Graph learning / Graph self-supervised learning
Scalable Graph Embedding for Asymmetric Proximity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| preserve asymmetric proximity with separate source/target vectors | graph embedding · cora biased negatives | AUC: 0.5360 -> 0.7674 | Helped |
| APP vs best symmetric embedding | graph embedding · arxiv link prediction | AUC: 0.8865 -> 0.8873 | No clear effect |
| APP vs best symmetric embedding | graph embedding · epinions link prediction | AUC: 0.8670 -> 0.9256 | Helped |
| score with source-target product instead of source-source (ablation) | APP · node recommendation | accuracy: 30% better | Helped |
| APP vs Adamic Adar | node recommendation · epinions | P@10: 0.026 vs 0.024 | No clear effect |
Closest papers by meaning
- Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding (AAAI 2017 · similarity 0.88)
- Residual2Vec: Debiasing graph embedding with random graphs (NeurIPS 2021 · similarity 0.88)
- Distance-Aware DAG Embedding for Proximity Search on Heterogeneous Graphs (AAAI 2018 · similarity 0.88)
- GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding (ICLR 2020 · similarity 0.87)
- Reconciling Competing Sampling Strategies of Network Embedding (NeurIPS 2023 · similarity 0.87)
- DeepWalking Backwards: From Embeddings Back to Graphs (ICML 2021 · similarity 0.87)
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