Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling

TL;DR

This paper investigates how different sampling distributions, especially centrality-based ones, influence the performance of Skip-Gram inspired graph embeddings, leading to faster learning and higher accuracy.

Contribution

It re-implements four main graph embedding techniques under a unified framework and analyzes the impact of sampling distributions on their effectiveness.

Findings

Centrality-based sampling improves embedding accuracy.

Using centrality distributions speeds up learning by up to 2 times.

Enhanced sampling methods consistently outperform traditional approaches.

Abstract

Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn low dimensional vector representations of the nodes in a graph by sampling node-context examples. Although many ways of sampling the context of a node have been proposed, the effects of the way a node is chosen have not been analyzed in depth. To fill this gap, we have re-implemented the main four word2vec inspired graph embedding techniques under the same framework and analyzed how different sampling distributions affects embeddings performance when tested in node classification problems. We present a set of experiments on different well known real data sets that show how the use of popular centrality distributions in sampling leads to improvements, obtaining speeds of up to 2 times in learning times and increasing accuracy in all cases.