Learning Graph Node Embeddings by Smooth Pair Sampling
This work addresses a specific bottleneck in graph embedding methods for researchers and practitioners, offering an incremental improvement.
The paper tackled the problem of skewed node pair frequencies in random walk-based graph node embeddings, which dominate learning, by proposing a new regularization technique using smoothed frequency sampling, resulting in demonstrated theoretical and experimental advantages.
Random walk-based node embedding algorithms have attracted a lot of attention due to their scalability and ease of implementation. Previous research has focused on different walk strategies, optimization objectives, and embedding learning models. Inspired by observations on real data, we take a different approach and propose a new regularization technique. More precisely, the frequencies of node pairs generated by the skip-gram model on random walk node sequences follow a highly skewed distribution which causes learning to be dominated by a fraction of the pairs. We address the issue by designing an efficient sampling procedure that generates node pairs according to their {\em smoothed frequency}. Theoretical and experimental results demonstrate the advantages of our approach.