TIMEST: Temporal Information Motif Estimator Using Sampling Trees
Summary: Scalable counting of temporal motifs in timestamped graphs where multiple timestamps per edge cause combinatorial explosion and existing methods fail beyond small motifs. TIMEST uses a temporal spanning-tree weighted sampler and randomized estimators with provable guarantees to count arbitrary-size temporal motifs, yielding massive speedups (e.g., 28x vs exact) and typically <5% error. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. Yunjie Pan
- 2. Omkar Bhalerao
- 3. C. Seshadhri
- 4. Nishil Talati
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,344 | Counting and Sampling Triangles from a Graph Stream | 2013 | VLDB | 0.00012473724 |
| 3,410 | Motivo: fast motif counting via succinct color coding and adaptive sampling | 2019 | VLDB | 7.1253867e-05 |
| 3,957 | 2SCENT: An Efficient Algorithm for Enumerating All Simple Temporal Cycles | 2018 | VLDB | 6.5903145e-05 |
| 4,459 | Efficient Bi-triangle Counting for Large Bipartite Networks | 2021 | VLDB | 6.1651553e-05 |
| 4,481 | Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs | 2024 | VLDB | 6.1485442e-05 |
| 4,626 | Efficient Biclique Counting in Large Bipartite Graphs | 2023 | SIGMOD | 6.0399035e-05 |
| 11,012 | Everest: GPU-Accelerated System For Mining Temporal Motifs | 2024 | VLDB | 4.1945683e-05 |
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