A Comparison between Recursive Neural Networks and Graph Neural Networks

By V. Massa, Gabriele Monfardini, L. Sarti, Franco Scarselli, Marco Maggini, Marco Gori. 2006

40 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Model Architectures, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ruslan Salakhutdinov
2. Abhinav Kumar Gupta
3. Yoshua Bengio
4. Richard S. Zemel
5. William L. Hamilton
6. Sergey Levine
7. Marc Brockschmidt
8. Li Fei-Fei

In Computer Vision and Pattern Recognition

1. Abhinav Kumar Gupta
2. Luc Van Gool
3. Dacheng Tao
4. Jitendra Malik
5. Ming-Hsuan Yang
6. Li Fei-Fei
7. Trevor Darrell
8. Xiaogang Wang

In Model Architectures

1. Ruslan Salakhutdinov
2. Alessio Micheli
3. Abhinav Kumar Gupta
4. Richard S. Zemel
5. Yujia Li
6. Marco Gori
7. Daniel Tarlow
8. Hendrik Blockeel

In Graph Learning

1. Abhinav Kumar Gupta
2. Marc Brockschmidt
3. William L. Hamilton
4. Ruslan Salakhutdinov
5. Richard S. Zemel
6. Daniel Tarlow
7. Kenneth Marino
8. Jure Leskovec

Citations in the corpus

• Gated Graph Sequence Neural Networks (2015, methodology): “, 2005; Di Massa et al., 2006; Scarselli et al., 2009; Uwents et al., 2011), but they do not appear to be in widespread use in the ICLR community. Part of our aim here is to publicize GNNs as a useful and interesting neural network variant. An analogy can be drawn between our adaptation from GNNs to”
• The Graph Neural Network Model (2009, neutral): “It is worth mentioning that the GNN model has been already successfully applied on larger applications, which include image classification and object localization in images [77], [78], web page ranking [79], relational learning [80], and XML classification [81].”
• The More You Know: Using Knowledge Graphs for Image Classification (2016, background): “There has been a lot of work in end-to-end learning on graphs or neural network trained on graphs [31, 2, 6, 11, 25, 22, 9, 21].”
• Neural Network for Graphs: A Contextual Constructive Approach (2009, methodology): “Finally, with respect to works initiated in [21] and [22], in the NN4G, the contextual information is incrementally added through the insertion of new units.”
• Neural networks for relational learning: an experimental comparison (2011, background): “…QSAR [Micheli et al., 2001], theorem proving [Goller, 1997], image classification and object localization in images [Bianchini et al., 2003, Di Massa et al., 2006], language recognition [Rodriguez, 2001, Sturt et al., 2003], logo recognition [Francesconi et al., 1998] and web page ranking…”