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Found 6 papers, 4 papers with code
Residual2Vec: Debiasing graph embedding with random graphs
Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs.
Unsupervised embedding of trajectories captures the latent structure of scientific migration
Here, we demonstrate the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories, producing an accurate, dense, continuous, and meaningful vector-space representation.
Constructing networks by filtering correlation matrices: A null model approach
Thresholding on the value of the pairwise correlation is probably the most straightforward and common method to create a network from a correlation matrix.
Physics and Society
Multiscale core-periphery structure in a global liner shipping network
We develop an algorithm to detect core-periphery pairs in a network, which allows one to find core and peripheral nodes on different scales and uses a configuration model that accounts for the fact that the network is obtained by the one-mode projection of a bipartite network.
Physics and Society
Solving Feature Sparseness in Text Classification using Core-Periphery Decomposition
We observe that prioritising features that are common to both training and test instances as cores during the CP decomposition to further improve the accuracy of text classification.
Finding multiple core-periphery pairs in networks
We propose a scalable algorithm to detect multiple non-overlapping groups of core-periphery structure in a network.
Physics and Society Social and Information Networks
