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LinkClus: Efficient Clustering via Heterogeneous Semantic Links

Summary: LinkClus clusters relational objects using recursively defined similarities over heterogeneous semantic links rather than intrinsic attributes. Its SimTree exploits power-law link structure and multi-granularity merging to avoid SimRank’s all-pairs costs while retaining accuracy and scalability. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
9648
Venue
VLDB
Year
2006
Pagerank
6.5614634e-05
Overall Rank
4,689 | 67.84%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{yin_vldb06,
        title = {{LinkClus: Efficient Clustering via Heterogeneous Semantic Links}},
        author = {Yin, Xiaoxin and Han, Jiawei and Yu, Philip S.},
        journal = {PVLDB},
        series = {{VLDB} '06},
        pages = {427--438},
        year = {2006}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
1,365 Scalable Similarity Search for SimRank 2014 SIGMOD 0.00011017467
1,588 More is Simpler: Effectively and Efficiently Assessing Node-Pair Similarities Based on Hyperlinks 2014 VLDB 0.0001026895
5,186 On Link-based Similarity Join 2011 VLDB 6.3279474e-05
5,416 BibNetMiner: Mining Bibliographic Information Networks 2008 SIGMOD 6.2255551e-05
5,774 DataScope: Viewing Database Contents in Google Maps' Way 2007 VLDB 6.0923864e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

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