Flow Provenance in Temporal Interaction Networks
Summary: Temporal interaction networks model entities exchanging quantities over time; this work traces flow origins at vertices (flow provenance). It formalizes flow-relay models for scenarios and offers provenance techniques to trace flow origins in dynamic networks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Chrysanthi Kosyfaki (University of Ioannina)
BibTeX Citation
@inproceedings{kosyfaki_sigmod21,
title = {{Flow Provenance in Temporal Interaction Networks}},
author = {Kosyfaki, Chrysanthi},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3450581},
url = {https://dl.acm.org/doi/10.1145/3448016.3450581},
year = {2021}
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,617 | Titian: Data Provenance Support in Spark | 2016 | VLDB | 0.00010209397 |
| 1,627 | Efficient Provenance Storage | 2008 | SIGMOD | 0.00010188097 |
| 1,798 | SMOKE: Fine-grained Lineage at Interactive Speed | 2018 | VLDB | 9.7361937e-05 |
| 2,851 | Fine-Grained, Secure and Efficient Data Provenance on Blockchain Systems | 2019 | VLDB | 8.0462369e-05 |
| 4,756 | Improving Reproducibility of Data Science Pipelines through Transparent Provenance Capture | 2020 | VLDB | 6.5221401e-05 |
| 4,845 | Explaining Outputs in Modern Data Analytics | 2016 | VLDB | 6.4818607e-05 |
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