FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism

Summary: FeLoG closes the sampling–training loop, prioritizing undertrained nodes via embedding-quality feedback to accelerate distributed graph embedding. Activity-aware communication and round-interleaved CPU–GPU pipelining cut communication and improve utilization, yielding 27.9× speedups. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID: h31418abed4f15d13
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,826 | 27.22%
DOI: 10.14778/3828612.3828633

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Peng Fang (Huazhong University of Science and Technology)
2. Arijit Khan (Bowling Green State University)
3. Ziqiang Wu (Huazhong University of Science and Technology)
4. Zhenli Li (Huazhong University of Science and Technology)
5. Yibo Zhou (Huazhong University of Science and Technology)
6. Fang Wang (Huazhong University of Science and Technology)
7. Dan Feng (Huazhong University of Science and Technology)

BibTeX Citation

@article{fang_vldb26,
        title = {{FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism}},
        author = {Fang, Peng and Khan, Arijit and Wu, Ziqiang and Li, Zhenli and Zhou, Yibo and Wang, Fang and Feng, Dan},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {10},
        pages = {2803--2816},
        doi = {10.14778/3828612.3828633},
        url = {https://doi.org/10.14778/3828612.3828633},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 16 of 16 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
211	AliGraph: A Comprehensive Graph Neural Network Platform	2019	VLDB	0.00024816965
773	Bitmap Index Design and Evaluation	1998	SIGMOD	0.00014084831
1,772	ByteGNN: Efficient Graph Neural Network Training at Large Scale	2022	VLDB	9.6792287e-05
1,835	Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank	2020	VLDB	9.5368647e-05
2,279	NeutronStar: Distributed GNN Training with Hybrid Dependency Management	2022	SIGMOD	8.7062637e-05
2,863	Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching	2022	VLDB	7.9301802e-05
4,825	DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning	2024	VLDB	6.3923554e-05
4,826	Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses	2024	VLDB	6.391978e-05
4,853	NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments	2024	VLDB	6.3805655e-05
5,124	SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization	2022	VLDB	6.2624516e-05
5,633	NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access	2022	SIGMOD	6.0578661e-05
6,109	GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs	2024	VLDB	5.8845577e-05
6,793	XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory Store	2024	VLDB	5.6810336e-05
8,064	Distributed Graph Embedding with Information-Oriented Random Walks	2023	VLDB	5.3954618e-05
8,152	NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism	2025	VLDB	5.3896512e-05
9,795	NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters	2026	SIGMOD	5.1257999e-05

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