We propose a method to represent bipartite networks using graph embeddings tailored to tackle the challenges of studying ecological networks, such as the ones linking plants and pollinators, where many covariates need to be accounted for, in particular to control for sampling bias. We adapt the v...
Variational autoencoder based bipartite network embedding by integrating local and global structureBipartite network embeddingLocal and global structureVariational autoencoderNonlinear structureAs a powerful tool for machine learning on the graph, network embedding, which projects nodes into low-dimensional ...
to solve graph semi-supervised learning Problem1(see Methods). Our Assumption1(see Methods) clarifies the capability of an autoencoder to obtain low-rank solution. Based on Assumption1, an autoencoder with manifold loss as we defined in
which was then enriched using CMap data. The proposed model was validated against gold-standard data and showed high potential in identifying novel therapeutic indications for existing drugs. Li and Lu [17] develop a novel bipartite graph model to infer drug-target indications based on drug pairwi...
network with a given number of layers is a blow up of a directed path on the same number of vertices. Such a graph is obtained by replacing each vertex of the path with an independent set of arbitrary but fixed size. The independent sets are then connected to form complete bipartite ...
We propose a novel end-to-end graph recommendation model called the Collaborative Variational Graph Auto-Encoder (CVGA), which uses the information propagation and aggregation paradigms to encode user鈥搃tem collaborative relationships on the user鈥搃tem interaction bipartite graph. These relationships ...
In particular, GraphEncoder proposed by Tian et al. [50] showed that optimizing the objective function of the autoencoder is similar to finding a solution for Spectral Clustering [50]. Leveraging on deep learning’s non-linearity and recent advances in Convolutional Neural Networks, Defferrard et...
BIPARTITE graphsThis study introduces a novel movie recommender system utilizing a Semantic-Enhanced Variational Graph Autoencoder for Movie Recommendation (SeVGAER) architecture. The system harnesses additional information from movie plot summaries scraped from the internet, transformed ...
Recently, [49] has proposed a variational autoencoder-based bipartite network model named BiVAE to integrate the global and local structural features of the network. However, it is a node embedding model designed explicitly for actual bipartite graph data without downstream tasks. Show abstract ...