Random-Walk Graph Embeddings and the Influence of Edge Weighting Strategies in Community Detection Tasks

Graph embedding methods have been developed over recent years with the goal of mapping graph data structures into low dimensional vector spaces so that conventional machine learning tasks can be efficiently evaluated. In particular, random walk based methods sample the graph using random walk sequences that capture a graph's structural properties. In this work, we study the influence of edge weighting strategies that bias the random walk process and we are able to demonstrate that under several settings the biased random walks enhance downstream community detection tasks.