Abstract
We study the effect of structural variation in graph data on the predictive performance
of graph kernels. To this end, we introduce a novel, noise-robust adaptation
of the GraphHopper kernel and validate it on benchmark data, obtaining modestly
improved predictive performance on a range of datasets. Next, we investigate the
performance of the state-of-the-art Weisfeiler-Lehman graph kernel under increasing
synthetic structural errors and find that the effect of introducing errors depends
strongly on the dataset.
of graph kernels. To this end, we introduce a novel, noise-robust adaptation
of the GraphHopper kernel and validate it on benchmark data, obtaining modestly
improved predictive performance on a range of datasets. Next, we investigate the
performance of the state-of-the-art Weisfeiler-Lehman graph kernel under increasing
synthetic structural errors and find that the effect of introducing errors depends
strongly on the dataset.
| Originalsprog | Engelsk |
|---|---|
| Titel | Neural Information Processing Systems 2017 |
| Redaktører | I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett |
| Antal sider | 5 |
| Forlag | NIPS Proceedings |
| Publikationsdato | 2017 |
| Status | Udgivet - 2017 |
| Begivenhed | 31st Annual Conference on Neural Information Processing Systems - Long Beach, USA Varighed: 4 dec. 2017 → 9 dec. 2017 Konferencens nummer: 31 |
Konference
| Konference | 31st Annual Conference on Neural Information Processing Systems |
|---|---|
| Nummer | 31 |
| Land/Område | USA |
| By | Long Beach |
| Periode | 04/12/2017 → 09/12/2017 |
| Navn | Advances in Neural Information Processing Systems |
|---|---|
| Vol/bind | 30 |
| ISSN | 1049-5258 |
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