Abstract:
Heterogeneous information networks (HINs) model objects of different types and their relationships. HIN objects usually have more attributes to enrich their data.
AHINs are attributed HINs. We cluster objects in an AHIN based on their structural connectedness and attribute values. We demonstrate how a must-link set and a cannot-link set can improve clustering results.
The SCHAIN algorithm solves the clustering problem, and two highly efficient variants, SCHAIN-PI and SCHAIN-IRAM, compute matrix eigenvectors using the power iteration-based method and the implicitly restarted Arnoldi method, respectively. We extensively test SCHAIN-based clustering algorithms. SCHAIN-IRAM outperforms competitors in clustering effectiveness and efficiency.
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