Conference paper Open Access

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

Kosmatopoulos, Andreas; Loumponias, Kostas; Chatzakou, Despoina; Tsikrika, Theodora; Vrochidis, Stefanos; Kompatsiaris, Ioannis

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.

Files (935.5 kB)
Name Size
2021_OASIS_Certh.pdf
md5:201e87be3f59a46619f1e73103613357
935.5 kB Download
17
16
views
downloads
Views 17
Downloads 16
Data volume 15.0 MB
Unique views 14
Unique downloads 12

Share

Cite as