Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning
Summary: Streaming vertex-cut partitioning for massive graphs via S5P, a skewness-aware method that splits edges into head/tail sets, clusters them, then solves a Stackelberg game to exploit degree skew. Yields better balance/communication than prior skew-oblivious streaming partitioners, up to 51% quality gain and 81% lower communication cost. (summarized by gpt-5.4-mini on May 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zezhong Ding
- 2. Yongan Xiang
- 3. Shangyou Wang
- 4. Xike Xie
- 5. S. Kevin Zhou
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,108 | DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training | 2025 | SIGMOD | 4.8297805e-05 |
| 9,804 | Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs | 2025 | SIGMOD | 4.2805224e-05 |
| 10,035 | SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine | 2026 | SIGMOD | 4.1945683e-05 |
| 10,885 | Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph | 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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