Deep Manifold Learning with Graph Mining
This work addresses performance issues in graph mining for domains like social networks, though it appears incremental as it builds on existing GCN frameworks.
The paper tackles the inefficiency and performance limitations of Graph Convolution Networks (GCNs) in semi-supervised learning by proposing a novel graph deep model with a non-gradient decision layer, achieving state-of-the-art results in experiments.
Admittedly, Graph Convolution Network (GCN) has achieved excellent results on graph datasets such as social networks, citation networks, etc. However, softmax used as the decision layer in these frameworks is generally optimized with thousands of iterations via gradient descent. Furthermore, due to ignoring the inner distribution of the graph nodes, the decision layer might lead to an unsatisfactory performance in semi-supervised learning with less label support. To address the referred issues, we propose a novel graph deep model with a non-gradient decision layer for graph mining. Firstly, manifold learning is unified with label local-structure preservation to capture the topological information of the nodes. Moreover, owing to the non-gradient property, closed-form solutions is achieved to be employed as the decision layer for GCN. Particularly, a joint optimization method is designed for this graph model, which extremely accelerates the convergence of the model. Finally, extensive experiments show that the proposed model has achieved state-of-the-art performance compared to the current models.