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| Author | Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes | ||||
| Title | Learning Graph Distances with Message Passing Neural Networks | Type | Conference Article | ||
| Year | 2018 | Publication | 24th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2239-2244 | ||
| Keywords | ★Best Paper Award★ | ||||
| Abstract | Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high
computational complexity, which makes it difficult to apply these matching algorithms in a real scenario. In this paper, we propose an efficient graph distance based on the emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a metric with a siamese network approach. The performance of the proposed graph distance is validated in two application cases, graph classification and graph retrieval of handwritten words, and shows a promising performance when compared with (approximate) graph edit distance benchmarks. |
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| Address | Beijing; China; August 2018 | ||||
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| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; 600.097; 603.057; 601.302; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RFL2018 | Serial | 3168 | ||
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