SGR: Self-Supervised Spectral Graph Representation Learning
This addresses the problem of flexible graph representation for diverse analytical tasks in machine learning, though it appears incremental as it builds on spectral analysis and self-supervised learning.
The paper tackles the challenge of representing graphs as vectors by developing SGR, a self-supervised spectral graph representation learning method that works across various domains and performs competitively with state-of-the-art methods without re-training.
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.