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MOSER: Scalable Network Motif Discovery using Serial Test

Summary: MOSER applies the serial test to provide statistical guarantees on motif sample quality rather than heuristic sampling. With two incremental subgraph-counting algorithms it scales NMD dramatically (up to 5 orders of magnitude) and improves downstream tasks like link prediction. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13737
Venue
VLDB
Year
2024
Pagerank
4.1945683e-05
Overall Rank
11,140 | 22.51%
DOI
10.14778/3632093.3632118

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Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
11,015 ZeroEA: A Zero-Training Entity Alignment Framework via Pre-Trained Language Model 2024 VLDB 4.1945683e-05
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Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
283 Querying K-Truss Community in Large and Dynamic Graphs 2014 SIGMOD 0.00029041257
1,844 Effective Community Search over Large Spatial Graphs 2017 VLDB 0.00010341077
11,492 On Analyzing Graphs with Motif-Paths 2021 VLDB 4.1945683e-05
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