  Graphs are a natural abstraction for many problems where nodes represent
entities and edges represent a relationship across entities. An important area
of research that has emerged over the last decade is the use of graphs as a
vehicle for non-linear dimensionality reduction in a manner akin to previous
efforts based on manifold learning with uses for downstream database
processing, machine learning and visualization. In this systematic yet
comprehensive experimental survey, we benchmark several popular network
representation learning methods operating on two key tasks: link prediction and
node classification. We examine the performance of 12 unsupervised embedding
methods on 15 datasets. To the best of our knowledge, the scale of our study --
both in terms of the number of methods and number of datasets -- is the largest
to date.
  Our results reveal several key insights about work-to-date in this space.
First, we find that certain baseline methods (task-specific heuristics, as well
as classic manifold methods) that have often been dismissed or are not
considered by previous efforts can compete on certain types of datasets if they
are tuned appropriately. Second, we find that recent methods based on matrix
factorization offer a small but relatively consistent advantage over
alternative methods (e.g., random-walk based methods) from a qualitative
standpoint. Specifically, we find that MNMF, a community preserving embedding
method, is the most competitive method for the link prediction task. While
NetMF is the most competitive baseline for node classification. Third, no
single method completely outperforms other embedding methods on both node
classification and link prediction tasks. We also present several drill-down
analysis that reveals settings under which certain algorithms perform well
(e.g., the role of neighborhood context on performance) -- guiding the
end-user.
