  Representing a graph as a vector is a challenging task; ideally, the
representation should be easily computable and conducive to efficient
comparisons among graphs, tailored to the particular data and analytical task
at hand. Unfortunately, a "one-size-fits-all" solution is unattainable, as
different analytical tasks may require different attention to global or local
graph features. We develop SGR, the first, to our knowledge, method for
learning graph representations in a self-supervised manner. Grounded on
spectral graph analysis, SGR seamlessly combines all aforementioned desirable
properties. In extensive experiments, we show how our approach works on large
graph collections, facilitates self-supervised representation learning across a
variety of application domains, and performs competitively to state-of-the-art
methods without re-training.
