Large Scale Graph Mining with G-Miner
Summary: G-Miner: a distributed graph-mining system demo for interactive analytics. Highlights workload challenges and design choices affecting performance, expressiveness, and usability, with empirical comparison to existing systems. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hongzhi Chen (Chinese University of Hong Kong)
- 2. Xiaoxi Wang (Chinese University of Hong Kong)
- 3. Chenghuan Huang (Chinese University of Hong Kong)
- 4. Juncheng Fang (Chinese University of Hong Kong)
- 5. Yifan Hou (Chinese University of Hong Kong)
- 6. Changji Li (Chinese University of Hong Kong)
- 7. James Cheng (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{chen_sigmod19,
title = {{Large Scale Graph Mining with G-Miner}},
author = {Chen, Hongzhi and Wang, Xiaoxi and Huang, Chenghuan and Fang, Juncheng and Hou, Yifan and Li, Changji and Cheng, James},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3320219},
url = {https://dl.acm.org/doi/10.1145/3299869.3320219},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,519 | G-Tran: A High Performance Distributed Graph Database with a Decentralized Architecture | 2022 | VLDB | 6.6472999e-05 |
| 6,408 | CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression | 2023 | SIGMOD | 5.8842681e-05 |
| 11,662 | Vertex-Centric Visual Programming for Graph Neural Networks | 2021 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 6,450 | Big Graph Analytics Systems | 2016 | SIGMOD | 5.8753826e-05 |
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|---|---|---|---|---|
| 1 | 9,685 | GARUDA: A System for Large-Scale Mining of Statistically Significant Connected Subgraphs | 2016 | VLDB |
| 2 | 5,328 | High Performance Distributed OLAP on Property Graphs with Grasper | 2020 | SIGMOD |
| 3 | 13,658 | Managing and Mining Large Graphs: Patterns and Algorithms | 2012 | SIGMOD |
| 4 | 8,789 | CrowdMiner: Mining association rules from the crowd | 2013 | VLDB |
| 5 | 5,833 | Managing and Mining Large Graphs: Systems and Implementations | 2012 | SIGMOD |
| 6 | 13,467 | The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability | 2021 | VLDB |
| 7 | 11,988 | Graph Data Mining with Arabesque | 2017 | SIGMOD |
| 8 | 11,942 | Declarative and distributed graph analytics with GRADOOP | 2018 | VLDB |
| 9 | 10,038 | GMine: A System for Scalable, Interactive Graph Visualization and Mining | 2006 | VLDB |
| 10 | 2,709 | GraphMiner: A Structural Pattern-Mining System for Large Disk-based Graph Databases and Its Applications | 2005 | SIGMOD |