Database Paper Browser

Back to papers

Prerequisite-driven Fair Clustering on Heterogeneous Information Networks

Summary: PDFC: prerequisite-driven fair clustering for heterogeneous information networks; meta-paths + prerequisite-meta-paths with Fairlets for balance. Orthogonal embeddings via Cholesky fusion; constrained k-means loss; dynamic adjacency updates for evolving HINs; validated on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6625
Venue
SIGMOD
Year
2023
Pagerank
4.1945683e-05
Overall Rank
11,193 | 22.14%
DOI
10.1145/3589267

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,219 F3 KM: Federated, Fair, and Fast k-means 2023 SIGMOD 4.1945683e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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

Previous Page 1 / 1 Next

Semantically Similar Papers