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Found 6 papers, 0 papers with code
Noise-robust classification with hypergraph neural network
This method is utilized to solve the noisy label learning problem.
To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods
To detect the irregular trade behaviors in the stock market is the important problem in machine learning field.
Solve fraud detection problem by using graph based learning methods
In this paper, we employ the graph p-Laplacian based semi-supervised learning methods combined with the undersampling techniques such as Cluster Centroids to solve the credit cards' fraud transactions detection problem.
PageRank algorithm for Directed Hypergraph
In this paper, we will model the World Wide Web's link structure as the directed hypergraph.
Hypergraph and protein function prediction with gene expression data
However, their average accuracy performance measures of these three methods are much greater than the average accuracy performance measures of un-normalized graph Laplacian based semi-supervised learning method (i. e. the baseline method of this paper) applied to gene co-expression network created from the gene expression data.
Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem
In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all yeast proteins in these multiple networks.
