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Found 6 papers, 5 papers with code
Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information
Cancer remains a global challenge due to its growing clinical and economic burden.
Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction
Understanding genetic variation, e. g., through mutations, in organisms is crucial to unravel their effects on the environment and human health.
RepBin: Constraint-based Graph Representation Learning for Metagenomic Binning
We solve the binning problem by developing new algorithms for (i) graph representation learning that preserves both homophily relations and heterophily constraints (ii) constraint-based graph clustering method that addresses the problems of skewed cluster size distribution.
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks.
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
It has achieved great success on many tasks of network analysis such as link prediction and node classification.
Deep Feature Learning of Multi-Network Topology for Node Classification
Network Embedding, aiming to learn non-linear and low-dimensional feature representation based on network topology, has been proved to be helpful on tasks of network analysis, especially node classification.
