TL;DR
This study proposes a model called the Residual compensation graph Convolutional Generalized Extreme Learning Machine Autoencoder (RCGELM-AE), which is built on the Extreme Learning Machine Autoencoder (ELM-AE) and incorporates two key designs, and offers a novel, gradient-descent-free solution for achieving efficient and effective graph embedding.
Experiments show that incorporating NC-LID-based regularization consistently improves reconstruction performance over the baseline without structural regularization and the method using hub-aware regularization, which highlights NC-LID as a useful structural signal for enhancing distance-based graph autoencoders in dyn...
Deep neural networks primarily aim to enhance the representation ability of high-level semantic features by deepening the network to reinforce critical features. However, this often leads to the under-utilization or discarding of certain low-level information, resulting in suboptimal performance. Due to differences in...
HCPN-GCN is proposed, a graph-aware extension of HPN that replaces the original linear feature extractors with Graph Convolutional Networks (GCNs) and introduces cone-based prototypes with a diversity regularization objective, which produces richer graph-aware representations while compactly modeling the embedding spac...
An innovative method for multi-view graph clustering named the graph convolution Transformer network with self-distillation (MvCConvFormer), which applies the Transformer architecture to effectively fuse the structural information of the optimal adjacency matrix and the node representations learned by the pre-training...
GraphVAEBM is introduced, a framework that separates graph generation from property modeling, and a Graph Variational Autoencoder is first trained to learn a continuous graph manifold and then frozen, while independent Energy-Based Models are trained to represent specific graph properties.
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