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Node embedding methods find latent lower-dimensional representations whichare used as features in machine learning models. In the last few years, thesemethods have become extremely popular as a replacement for manual featureengineering. Since authors use various approaches for the evaluation of nodeembedding methods, existing studies can rarely be efficiently and accuratelycompared. We address this issue by developing a process for a fair andobjective evaluation of node embedding procedures w.r.t. node classification.This process supports researchers and practitioners to compare new and existingmethods in a reproducible way. We apply this process to four popular nodeembedding methods and make valuable observations. With an appropriatecombination of hyperparameters, good performance can be achieved even withembeddings of lower dimensions, which is positive for the run times of thedownstream machine learning task and the embedding algorithm. Multiplehyperparameter combinations yield similar performance. Thus, no extensive,time-consuming search is required to achieve reasonable performance in mostcases.

A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification