For the purpose of learning on graphs, we hunt for a graph feature representation that exhibit certain uniqueness, stability and sparsity properties while also being amenable to fast computation. This leads to the discovery of family of graph spectral distances (denoted as FGSD) and their based graph feature representations, which we prove to possess most of these desired properties. To both evaluate the quality of graph features produced by FGSD and demonstrate their utility, we apply them to the graph classification problem. Through extensive experiments, we show that a simple SVM based classification algorithm, driven with our powerful FGSD based graph features, significantly outperforms all the more sophisticated state-of-art algorithms on the unlabeled node datasets in terms of both accuracy and speed; it also yields very competitive results on the labeled datasets - despite the fact it does not utilize any node label information.
|Original language||English (US)|
|Number of pages||11|
|Journal||Advances in Neural Information Processing Systems|
|State||Published - 2017|
|Event||31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States|
Duration: Dec 4 2017 → Dec 9 2017
Bibliographical noteFunding Information:
This research was supported in part by ARO MURI Award W911NF-12-1-0385, DTRA grant HDTRA1-14-1-0040, and NSF grants CNS-1618339, CNS-1618339 and CNS-1617729.