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Found 6 papers, 1 papers with code
THESAURUS: Contrastive Graph Clustering by Swapping Fused Gromov-Wasserstein Couplings
However, due to their reliance on K-means, these methods inherit its drawbacks when the cluster separability of encoder output is low, facing challenges from the Uniform Effect and Cluster Assimilation.
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
This capability implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes.
Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training
However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms face difficulties in acquiring the necessary community-level information, resulting in poor performance.
Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering
To well cope with the issues, we propose a hyper-Laplacian regularized concept factorization (HLRCF) in low-rank tensor space for multi-view clustering.
Multi-view Graph Convolutional Networks with Differentiable Node Selection
Due to the powerful capability to gather information of neighborhood nodes, in this paper, we apply Graph Convolutional Network (GCN) to cope with heterogeneous-graph data originating from multi-view data, which is still under-explored in the field of GCN.
Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning
In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms.
