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Found 2 papers, 1 papers with code
DINE: Dimensional Interpretability of Node Embeddings
Towards that, we first develop new metrics that measure the global interpretability of embedding vectors based on the marginal contribution of the embedding dimensions to predicting graph structure.
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative Sampling
Representation learning models for graphs are a successful family of techniques that project nodes into feature spaces that can be exploited by other machine learning algorithms.
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