Unsupervised graph embedding algorithm based on Gaussian distribution and Householder flow
In order to express nodes better,a novel graph embedding method was proposed,each node was represented as a Gaussian distribution composed of mean and variance.By applying a series of reversible Householder transformations,the relatively simple distribution was transformed into a more flexible distribution,so that the uncertainty about its representation could be better captured.To improve stability,Wasserstein distance was used to measure between distributions.The experimental results showed that on multiple benchmark datasets,Graph2Gauss(G2G)algorithm using Householder transformation performed better than the original model on link prediction.It could be seen from the effectiveness of node classification that using Wasserstein distance could significantly increase the F1 score for graphs with missing node information.
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