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LinkClus: Efficient Clustering via Heterogeneous Semantic Links

Summary: LinkClus uses heterogeneous semantic links for clustering, via SimTree-based multi-granularity similarity; exploits power-law distributions. Merges traversal to avoid pairwise computations, enabling scalable clustering of multi-typed linked objects. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
9457
Venue
VLDB
Year
2006
Pagerank
6.2758722e-05
Overall Rank
4,342 | 69.80%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
1,539 Scalable Similarity Search for SimRank 2014 SIGMOD 0.00011460415
1,903 More is Simpler: Effectively and Efficiently Assessing Node-Pair Similarities Based on Hyperlinks 2014 VLDB 0.00010155777
3,961 BibNetMiner: Mining Bibliographic Information Networks 2008 SIGMOD 6.5876809e-05
4,995 On Link-based Similarity Join 2011 VLDB 5.7787414e-05
5,769 DataScope: Viewing Database Contents in Google Maps’ Way 2007 VLDB 5.3333819e-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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