Name
Abstract | ||
|---|---|---|
A multitude of important real-world or synthetic systems possess network structures. Extending learning techniques such as neural networks to process such non-Euclidean data is therefore an important direction for machine learning research. However, this domain has received comparatively low levels of attention until very recently. There is no straight-forward application of machine learning to network data, as machine learning tools are designed for i.i.d data, simple Euclidean data, or grids. To address this challenge, the technical focus of this dissertation is on the use of graph neural networks for network representation learning (NRL); i.e.. learning the vector representations of nodes in networks. Learning the vector embeddings of graph-structured data is similar to embedding complex data into low-dimensional geometries. After the embedding process is completed, the drawbacks associated with graph-structured data are overcome. The current inquiry proposes two deep-learning auto-encoder-based approaches for generating node embeddings. The drawbacks in such existing auto-encoder approaches as shallow architectures and excessive parameters are tackled in the proposed architectures by using fully convolutional layers. Extensive experiments are performed on publicly available benchmark network datasets to highlight the validity of this approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.7494/csci.2019.20.3.3167 | COMPUTER SCIENCE-AGH |
Keywords | Field | DocType |
network representation learning, deep learning, graph convolutional neural networks | Embedding,Computer science,Auto encoders,Complex data type,Artificial intelligence,Euclidean geometry,Deep learning,Artificial neural network,Cluster analysis,Feature learning,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 3 | 1508-2806 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pranav Nerurkar | 1 | 5 | 2.02 |
| Madhav Chandane | 2 | 0 | 0.34 |
| Sunil G. Bhirud | 3 | 15 | 3.11 |