Community Level Diffusion Extraction
Summary: Proposes COLD (Community Level Diffusion): a unified model of topics and communities to uncover inter-community influence beyond user diffusion. GraphLab-based parallel inference delivers scalable, accurate diffusion predictions and enables new analysis of community roles by interest. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhiting Hu (Carnegie Mellon University; Peking University)
- 2. Junjie Yao (East China Normal University)
- 3. Bin Cui (Peking University)
- 4. Eric P. Xing (Carnegie Mellon University)
BibTeX Citation
@inproceedings{hu_sigmod15,
title = {{Community Level Diffusion Extraction}},
author = {Hu, Zhiting and Yao, Junjie and Cui, Bin and Xing, Eric P.},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2723737},
url = {https://dl.acm.org/doi/10.1145/2723372.2723737},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 12,023 | From Community Detection to Community Profiling | 2017 | VLDB | 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 |
|---|---|---|---|---|
| 20 | Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud | 2012 | VLDB | 0.00056944564 |
| 194 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD | 0.000259047 |
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