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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
12489
Venue
VLDB
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,690 | 19.80%
DOI
10.14778/3447689.3447714

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@article{li_vldb21,
        title = {{On Analyzing Graphs with Motif-Paths}},
        author = {Li, Xiaodong and Cheng, Reynold and Chang, Kevin Chen-Chuan and Shan, Caihua and Ma, Chenhao and Cao, Hongtai},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {6},
        pages = {1111--1123},
        doi = {10.14778/3447689.3447714},
        url = {https://doi.org/10.14778/3447689.3447714},
        year = {2021}
}

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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
189 Querying K-Truss Community in Large and Dynamic Graphs 2014 SIGMOD 0.00026114928
273 Online Search of Overlapping Communities 2013 SIGMOD 0.00022671795
1,378 Effective Community Search over Large Spatial Graphs 2017 VLDB 0.00010971508
1,958 A General Framework for Estimating Graphlet Statistics via Random Walk 2017 VLDB 9.4093057e-05
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