Name
Abstract | ||
|---|---|---|
The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs. Classifying and clustering subgraphs according to their functionality is useful to discover functionally equivalent engineering artifacts that exhibit different graph structures. This paper presents a new graph learning algorithm designed to classify engineering data artifacts -- represented in the form of graphs -- according to their structure and neighborhood features. Our Structural Graph Convolutional Neural Network (SGCNN) is capable of learning graphs and subgraphs with a novel graph invariant convolution kernel and downsampling/pooling algorithm. On a realistic engineering-related dataset, we show that SGCNN is capable of achieving ~91% classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3240765.3243477 | arXiv: Artificial Intelligence |
Field | DocType | Volume |
Data mining,Graph,Graph property,Convolutional neural network,Computer science,Pooling,Theoretical computer science,Automation,Upsampling,Cluster analysis,Kernel (image processing) | Journal | abs/1808.08213 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiang Wan | 1 | 45 | 5.63 |
| Blake S. Pollard | 2 | 0 | 1.01 |
| Sujit Rokka Chhetri | 3 | 2 | 1.42 |
| Palash Goyal | 4 | 14 | 2.90 |
| Mohammad Abdullah Al Faruque | 5 | 627 | 65.35 |
| Arquimedes Canedo | 6 | 143 | 23.31 |