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On Analyzing Graphs with Motif-Paths

Summary: Motif-paths, concatenations of motif instances, offer high-order structure for graph analysis beyond traditional edge-based approaches. The work applies motif-paths to link prediction, local clustering, and node ranking, and introduces a defragmentation method to connect previously disconnected motif-paths, with experiments on real graphs showing improved effectiveness. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12301
Venue
VLDB
Year
2021
Pagerank
4.1945683e-05
Overall Rank
11,492 | 20.06%
DOI
10.14778/3447689.3447714

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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
370 Online Search of Overlapping Communities 2013 SIGMOD 0.00025415479
1,740 A General Framework for Estimating Graphlet Statistics via Random Walk 2017 VLDB 0.0001071792
1,844 Effective Community Search over Large Spatial Graphs 2017 VLDB 0.00010341077
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