Graph Data Neural Network (GNN)
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A Graph Data Neural Network (GNN) is a artificial neural network for graph data.
- Context:
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- Example(s):
- Counter-Example(s):
- DNN for Sequential Data, such as Image Neural Network, and Language Neural Network.
- See: GNN Training System, GNN Training Algorithm.
References
2024
- (Shabani et al., 2024) ⇒ Nasrin Shabani, Jia Wu, Amin Beheshti, Quan Z. Sheng, Jin Foo, Venus Haghighi, Ambreen Hanif, and Maryam Shahabikargar. (2024). “A Comprehensive Survey on Graph Summarization with Graph Neural Networks.” IEEE Transactions on Artificial Intelligence.
2020a
- (Zhou et al., 2020a) ⇒ Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun. "Graph Neural Networks: A Review of Methods and Applications". In: AI Open.
2020b
- (Bojchevski et al., 2020) ⇒ Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. (2020). “Scaling Graph Neural Networks with Approximate PageRank.” In: arXiv preprint arXiv:2007.01570.
- QUOTE: Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge - many recently proposed scalable GNN approaches rely on an expensive message-passing procedure to propagate information through the graph. ...
2020c
- (Huang et al., 2020) ⇒ Kexin Huang, Cao Xiao, Lucas M. Glass, Marinka Zitnik, and Jimeng Sun (2020) "SkipGNN: predicting molecular interactions with skip-graph networks". Scientific Reports volume 10.
2020d
- (Wu et al., 2020) ⇒ Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S. Yu Philip. (2020). “A Comprehensive Survey on Graph Neural Networks.” IEEE transactions on neural networks and learning systems 32, no. 1
- NOTE: The paper provides a comprehensive survey on graph neural networks (GNNs). The authors propose a new taxonomy that categorizes GNNs into four groups: recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks.
2019a
- (Wang et al., 2019) ⇒ Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. (2019). “Neural Graph Collaborative Filtering.” In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval.
- QUOTE: Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. ... In this work, we propose to integrate the user-item interactions --- more specifically the bipartite graph structure --- into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in user-item graph, effectively injecting the collaborative signal into the embedding process in an explicit manner.
2019b
- (Ying et al., 2019b) ⇒ Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec (2019b)."GNNExplainer: Generating Explanations for Graph Neural Networks". In: Adv Neural Inf Process Syst.
2018
- (Ying et al., 2018) ⇒ Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. (2018). “Graph Convolutional Neural Networks for Web-scale Recommender Systems.” In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.