Higher-Order Spectral Clustering for Geometric Graphs
Keyword(s):
AbstractThe present paper is devoted to clustering geometric graphs. While the standard spectral clustering is often not effective for geometric graphs, we present an effective generalization, which we call higher-order spectral clustering. It resembles in concept the classical spectral clustering method but uses for partitioning the eigenvector associated with a higher-order eigenvalue. We establish the weak consistency of this algorithm for a wide class of geometric graphs which we call Soft Geometric Block Model. A small adjustment of the algorithm provides strong consistency. We also show that our method is effective in numerical experiments even for graphs of modest size.
2020 ◽
Vol 375
◽
pp. 112795
Keyword(s):
2013 ◽
Vol 237
(1)
◽
pp. 145-161
◽
2017 ◽
Vol 10
(11)
◽
pp. 11-22
2021 ◽
Vol 5
(3)
◽
pp. 315
Keyword(s):
2021 ◽
Vol 126
◽
pp. 106495