In this section, we introduce the related works about graph partitioning, graph neural networks, graph similarity metrics and graph similarity computation. The proposed approach: PSimGNN In this section, we formally define the problem of graph similarity computation, and then introduce the proposed met...
Fast spectral clustering learning with hierarchical bipartite graph for large-scale data Spectral clustering (SC) is drawing more and more attention due to its effectiveness in unsupervised learning. However, all of these methods still have lim... X Yang,W Yu,R Wang,... - 《Pattern Recognition...
Due to the utilisation of bipartite graph; decomposition and heterogeneous message propagation; this technique successfully captures intricate user-item interactions. Systems uses graph convolutional networks (GCN) and meta-path-based graph neural networks (GNN) to store features that make the user-item...
Besides, the traditional Graph Neural Networks (GNNs) fail to consider meaningful edge features and are difficult to perform heterogeneous graphs embedding. To overcome the limitations of the existing approaches, we present a hierarchical approach for APT detection with novel attention-based GNNs. We ...
Sun et al. proposed a new framework that can explicitly aggregate neighbor node information, efficiently modeling user–item bipartite heterogeneous graphs [48]. In the online recommendation services, Xu et al. developed a framework based on incremental learning graph neural networks to address the ...
Neural graph collaborative filtering (NGCF) [4] treats the collaborative relationship between users and items as a bipartite graph, which explicitly encodes higher-order collaborative signals. Knowledge graph attention network (KGAT) [23] combines the user-item graph with the knowledge graph, using a...
In the case of a bipartite graph between V and H with all variables binary, the hierarchical model (or rather its visible marginal) is a restricted Boltzmann machine, denoted RBMV,H. This model is illustrated in Fig. 4. As shown in (7), the free energy takes the formF(x)=∑i∈Vb...
IEEE Transactions on Neural Networks 1999, 10(3):626-634. 10.1109/72.761722 Article Google Scholar Zhen T, Zhenjiang M: Fast background subtraction using improved GMM and graph cut. Proceedings of the 1st International Congress on Image and Signal Processing (CISP '08), 2008, Sanya, China 4...