Robustness of graph embedding methods for community detection

arXiv:2405.00636v1 Announce Type: cross
Abstract: This study investigates the robustness of graph embedding methods for community detection in the face of network perturbations, specifically edge deletions. Graph embedding techniques, which represent nodes as low-dimensional vectors, are widely used for various graph machine learning tasks due to their ability to capture structural properties of networks effectively. However, the impact of perturbations on the performance of these methods remains relatively understudied. The research considers state-of-the-art graph embedding methods from two families: matrix …

abstract arxiv community cs.lg cs.si detection edge embedding face graph low machine machine learning network nodes physics.data-an physics.soc-ph robustness study tasks type vectors