Graph Learning Network (GLN)

dc.contributor	Saire Pilco, Darwin
dc.creator	Saire Pilco, Darwin Danilo
dc.creator	Ramirez Rivera, Gerberth Adin
dc.date.accessioned	2022-12-16T13:21:51Z
dc.date.available	2022-12-16T13:21:51Z
dc.identifier	https://doi.org/10.25824/redu/B3XYDD
dc.identifier.uri	https://repositorioslatinoamericanos.uchile.cl/handle/2250/5363077
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.publisher	Repositório de Dados de Pesquisa da Unicamp
dc.subject	Computer and Information Science
dc.subject	Graph neural network
dc.subject	Pattern recognition
dc.subject	Deep learning
dc.subject	Machine learning
dc.title	Graph Learning Network (GLN)