Jennifer L Brame, Rocio B Quinonez, Brittney P Ciszek, Jane A Weintraub
Implementing a Prenatal Oral Health Program for Dental Students: Lessons Learned
Health promotion practice
light exponential growth information volume significance graph data intensified graph clustering plays pivotal role graph data processing jointly modeling graph structure node attributes notably practical significance multiview graph clustering heightened due presence diverse relationships within realworld graph data nonetheless prevailing graph clustering techniques predominantly grounded deep learning neural networks face challenges effectively handling multiview graph data challenges include incapability concurrently explore relationships multiple view structures node attributes well difficulties processing multiview graph data varying features tackle issues research proposes straightforward yet effective multiview graph clustering approach known slmgc approach uses graph filtering filter noise reduces computational complexity extracting samples based node importance enhances clustering representations graph contrastive regularization achieves final clustering outcomes using selftraining clustering algorithm notably unlike neural network algorithms approach avoids need intricate parameter settings comprehensive experiments validate supremacy slmgc approach multiview graph clustering endeavors contrasted prevailing deep neural network techniques