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Found 5 papers, 4 papers with code
StATIK: Structure and Text for Inductive Knowledge Graph Completion
Knowledge graphs (KGs) often represent knowledge bases that are incomplete.
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural Networks
This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature.
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
We propose Graph Traversal via Tensor Functionals(GTTF), a unifying meta-algorithm framework for easing the implementation of diverse graph algorithms and enabling transparent and efficient scaling to large graphs.
Distributed Training of Graph Convolutional Networks using Subgraph Approximation
Modern machine learning techniques are successfully being adapted to data modeled as graphs.
