… problem of classifying sparsely labeled networks, where labeled nodes in the network are
… [3] proposed to add “ghost edges” created via random walk with restart, to a network, which …

… classification accuracy. Most such prior research has assumed the provision of a densely
labeled training network. … case when LBC must use a single sparsely labeled network for both …

… network with sparse links/edges spanning different misinformation topics across nine
categories. Nodes in the … Faloutsos, ”Using Ghost Edges for Classification in Sparsely Labeled …

… in sparsely labeled networks, our method wvGN exploits not only local but also global
neighborhood information of vertices. … ghostEdge [8] works by adding ghost edges to a network to …

… input data, such as edges and embosses, while … ghost-SE-ghost without shortcut connection
(denoted as NoSC, ie, we have removed only the shortcut connection only), the ghost–ghost …

… of classification in extremely sparsely labeled network, we … the newly labeled nodes will be
added to the labeled node set and … , “Using ghost edges for classification in sparsely labeled …

A new deep learning-based classification model called the Stochastic Dilated Residual Ghost
(SDRG) was proposed in this work for categorizing histopathology images of breast cancer…

… Using ghost edges for classification in sparsely labeled networks. In SIGKDD. 256–264. …
Since RS-GNN aims to densify the graphs to benefit predictions in sparsely labeled graphs, we …

… normalization to improve the image quality for breast cancer classification. Data augmentation
ran cutting-edge deep learning models and delivered promising outcomes by solving over…

… In situations where we have a sparsely labelled network and do … regular label propagation
and the ghost edges method. … We see that the ghost method scales with number of labelled …