GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs
Summary: GENTI: GPU-oriented walk-based SGRL for dynamic graphs, decoupling subgraph extraction into CPU neighbor sampling and asynchronous GPU subgraph gathering with bespoke dynamic-graph structures. Delivers up to 30x extraction and 26x end-to-end speedups, scaling to 1.3B edges. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zihao Yu
- 2. Ningyi Liao
- 3. Siqiang Luo
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,011 | A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness | 2026 | SIGMOD | 4.1945683e-05 |
| 10,035 | SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine | 2026 | SIGMOD | 4.1945683e-05 |
| 10,322 | Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation | 2026 | VLDB | 4.1945683e-05 |
| 10,673 | When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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