You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Another related area of research concerns deep learning architectures on graph-structured data.The GNN was first proposed in Gori et al. (2005); Scarselli et al. (2009), as a trainable recurrent message-passingwhose fixed points could be adjusted discriminatively.Subsequent works Li et al. (2015); Sukhbaatar et al. (2016) have relaxed the model by untying the recurrent layer weights and proposed several nonlinear updates through gating mechanisms.Graph neural networks are in fact natural generalizations of convolutional networks to non-Euclidean graphs. Bruna et al. (2013); Henaff et al. (2015) proposed to learn smooth spectral multipliers of the graph Laplacian, albeit with high computational cost, and Defferrard et al. (2016); Kipf & Welling (2016) resolved the computational bottleneck by learning polynomials of the graph Laplacian, thus avoiding the computation of eigenvectors and completing the connection with GNNs. In particular, Kipf & Welling (2016)was the first to propose the use of GNNs on semi-supervised classification problems.We refer the reader to Bronstein et al. (2017) for an exhaustive literature review on the topic.GNNs and the analogous Neural Message Passing Models are finding application in many different domains. Battaglia et al. (2016); Chang et al. (2016) develop graph interaction networks that learn pairwise particle interactions and apply them to discrete particle physical dynamics. Duvenaud et al. (2015); Kearnes et al. (2016) study molecular fingerprints using variants of the GNN architecture, and Gilmer et al. (2017) further develop the model by combining it with set representations Vinyals et al. (2015), showing state-of-the-art results on molecular prediction.

Question: Does previous researches, which paper mentioned, using GNN?
Previous researches such as Duvenaud et al. (2015)and Kearnes et al. (2016) used GNN.