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Found 5 papers, 4 papers with code
Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness
Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis.
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues.
Prototypical Graph Contrastive Learning
However, since for a query, its negatives are uniformly sampled from all graphs, existing methods suffer from the critical sampling bias issue, i. e., the negatives likely having the same semantic structure with the query, leading to performance degradation.
Data-to-Text Generation with Style Imitation
That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.
Iterative Graph Self-Distillation
Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs.
