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Found 3 papers, 2 papers with code
Federated Learning over Coupled Graphs
In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
Meta Reinforcement Learning with Task Embedding and Shared Policy
Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.
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