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Found 6 papers, 2 papers with code
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes; Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node-pair and a dissimilar node-pair.
Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications
The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used in spectral clustering and community detection.
Name Disambiguation in Anonymized Graphs using Network Embedding
In real-world, our DNA is unique but many people share names.
Trust from the past: Bayesian Personalized Ranking based Link Prediction in Knowledge Graphs
Link prediction, or predicting the likelihood of a link in a knowledge graph based on its existing state is a key research task.
Incremental Method for Spectral Clustering of Increasing Orders
The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection.
Feature Selection for Classification under Anonymity Constraint
Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously.
