COMMIT: A Scalable Approach to Mining Communication Motifs from Dynamic Networks
Summary: COMMIT scales mining of communication motifs in dynamic networks by turning evolving graphs into a sequence database and pruning the search. Up to 100x speedups vs baselines; motifs reveal recurring interaction patterns and social-network influence. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Saket Gurukar
- 2. Sayan Ranu
- 3. Balaraman Ravindran
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,208 | Mining Bursting Core in Large Temporal Graphs | 2022 | VLDB | 6.357214e-05 |
| 4,481 | Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs | 2024 | VLDB | 6.1485442e-05 |
| 4,970 | On Querying Connected Components in Large Temporal Graphs | 2023 | SIGMOD | 5.7945079e-05 |
| 7,883 | Towards Plug-and-Play Visual Graph Query Interfaces: Data-driven Selection of Canned Patterns for Large Networks | 2021 | VLDB | 4.6282138e-05 |
| 8,210 | Mining Top-k Pairs of Correlated Subgraphs in a Large Network | 2020 | VLDB | 4.5581054e-05 |
| 8,242 | Hunting Temporal Bumps in Graphs with Dynamic Vertex Properties | 2022 | SIGMOD | 4.551877e-05 |
| 8,809 | Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information Networks | 2024 | VLDB | 4.4443756e-05 |
| 10,563 | Efficient Historical Butterfly Counting in Large Temporal Bipartite Networks via Graph Structure-aware Index | 2025 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 951 | Comparing Stars: On Approximating Graph Edit Distance | 2009 | VLDB | 0.00015106325 |
| 1,089 | GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph | 2014 | VLDB | 0.00014157922 |
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