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Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank
Summary: Links T-GNN temporal message passing to temporal random walks and proposes T-PPR, a parameterized influence score showing a few temporal neighbors suffice for accurate representations. Introduces Zebra: a scalable framework with a single-scan top-k T-PPR algorithm (provable approximation) that aggregates top influencers, delivering up to 100× speedups and often better accuracy.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 12999
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 6.8176818e-05
- Overall Rank
- 3,715 | 74.19%
- DOI
-
10.14778/3583140.3583150
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 19 of 19 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,356 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5514335e-05 |
| 5,485 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4817019e-05 |
| 5,721 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3538607e-05 |
| 6,944 |
Efficient Training of Graph Neural Networks on Large Graphs |
2024 |
VLDB |
4.8875946e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8570865e-05 |
| 7,286 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7701372e-05 |
| 7,750 |
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs |
2024 |
VLDB |
4.6565447e-05 |
| 8,507 |
Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense |
2024 |
VLDB |
4.4909322e-05 |
| 9,277 |
Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs |
2023 |
VLDB |
4.3610659e-05 |
| 9,486 |
Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent Queries |
2024 |
VLDB |
4.3300131e-05 |
| 9,677 |
Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving |
2025 |
SIGMOD |
4.3006524e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,334 |
Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation |
2026 |
VLDB |
4.1905499e-05 |
| 10,642 |
PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization |
2025 |
VLDB |
4.1905499e-05 |
| 10,664 |
Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing |
2025 |
VLDB |
4.1905499e-05 |
| 10,681 |
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction |
2025 |
VLDB |
4.1905499e-05 |
| 10,889 |
Efficient Graph Embedding Generation and Update for Large-Scale Temporal Graph |
2025 |
VLDB |
4.1905499e-05 |
| 10,891 |
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours |
2025 |
VLDB |
4.1905499e-05 |
| 11,030 |
BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRank |
2024 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 478 |
Fast Incremental and Personalized PageRank |
2011 |
VLDB |
0.00022183187 |
| 635 |
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding |
2021 |
SIGMOD |
0.00018825777 |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 1,626 |
Fast and Exact Top-k Search for Random Walk with Restart |
2012 |
VLDB |
0.0001108491 |
| 1,907 |
Fast and Unified Local Search for Random Walk Based K-Nearest-Neighbor Query in Large Graphs |
2014 |
SIGMOD |
0.00010130702 |
| 2,076 |
Efficient Ad-hoc Search for Personalized PageRank |
2013 |
SIGMOD |
9.6057342e-05 |
| 2,110 |
HubPPR: Effective Indexing for Approximate Personalized PageRank |
2017 |
VLDB |
9.5280826e-05 |
| 2,522 |
Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push |
2021 |
SIGMOD |
8.6029608e-05 |
| 4,126 |
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization |
2021 |
VLDB |
6.4242e-05 |
| 4,670 |
Realtime Top-k Personalized PageRank over Large Graphs on GPUs |
2020 |
VLDB |
6.0027844e-05 |
| 4,688 |
TopPPR: Top-k Personalized PageRank Queries with Precision Guarantees on Large Graphs |
2018 |
SIGMOD |
5.9900111e-05 |
| 5,237 |
Edge-based Local Push for Personalized PageRank |
2022 |
VLDB |
5.6071758e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 4,670 |
Realtime Top-k Personalized PageRank over Large Graphs on GPUs |
2020 |
VLDB |
6.0027844e-05 |
| 4,688 |
TopPPR: Top-k Personalized PageRank Queries with Precision Guarantees on Large Graphs |
2018 |
SIGMOD |
5.9900111e-05 |
| 5,693 |
Parallel Personalized PageRank on Dynamic Graphs |
2018 |
VLDB |
5.3683002e-05 |
| 5,666 |
Personalized PageRank on Evolving Graphs with an Incremental Index-Update Scheme |
2023 |
SIGMOD |
5.3824583e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8570865e-05 |
| 1,528 |
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank |
2020 |
VLDB |
0.00011489661 |
| 9,277 |
Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs |
2023 |
VLDB |
4.3610659e-05 |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 10,891 |
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours |
2025 |
VLDB |
4.1905499e-05 |
| 10,681 |
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction |
2025 |
VLDB |
4.1905499e-05 |