| dc.contributor.author |
Saire Pilco, Darwin Danilo |
|
| dc.contributor.author |
Ramirez Rivera, Gerberth Adin |
|
| dc.date |
2021-03-01 |
|
| dc.date.accessioned |
2021-02-24 |
|
| dc.identifier.uri |
https://doi.org/10.25824/redu/B3XYDD |
|
| dc.identifier.uri |
https://redu.unicamp.br/dataset.xhtml?persistentId=doi:10.25824/redu/B3XYDD |
|
| dc.description |
GLN creates a set of node embeddings H(l) that are later combined to produce an intermediary representation H_int(l). Then, we use the updated node information with the adjacency information to produce a local embedding of the nodes' information H_local(l) that is also the output H(l+1). We also broadcast the information of the local embedding to produce a global embedding H_global(l). We combine the local and global embeddings to predict the next layer adjacency A(l+1).
Additionally, we create three Synthetic Graph Datasets: the 3D-Surface, Community, and Geometric Figures.
The source code is available in the public repository https://gitlab.com/mipl/graph-learning-network and the datasets are available in https://gitlab.com/mipl/graph-learning-network/-/tree/master/datasets. |
|
| dc.description.sponsorship |
Fundação de Amparo à Pesquisa do Estado de São Paulo |
|
| dc.publisher |
Saire Pilco, Darwin |
|
| dc.subject |
Computer and Information Science |
|
| dc.title |
Graph Learning Network (GLN) |
|
| dc.description.sponsorshipId |
FAPESP: 2017/16597-7 |
|