Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Benchmarking Network Embedding Models for Link Prediction: Are We Making
  Progress?   Network embedding methods map a network's nodes to vectors in an embedding
space, in such a way that these representations are useful for estimating some
notion of similarity or proximity between pairs of nodes in the network. The
quality of these node representations is then showcased through results of
downstream prediction tasks. Commonly used benchmark tasks such as link
prediction, however, present complex evaluation pipelines and an abundance of
design choices. This, together with a lack of standardized evaluation setups
can obscure the real progress in the field. In this paper, we aim to shed light
on the state-of-the-art of network embedding methods for link prediction and
show, using a consistent evaluation pipeline, that only thin progress has been
made over the last years. The newly conducted benchmark that we present here,
including 17 embedding methods, also shows that many approaches are
outperformed even by simple heuristics. Finally, we argue that standardized
evaluation tools can repair this situation and boost future progress in this
field.

28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th