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In network theory, link prediction is the problem of predicting the existence of a link between two entities in a network. Examples of link prediction include predicting friendship links among users in a social network, predicting co-authorship links in a citation network, and predicting interactions between genes and proteins in a biological network. Link prediction can also have a temporal aspect, where, given a snapshot of the set of links at time , the goal is to predict the links at time . Link prediction is widely applicable. In e-commerce, link prediction is often a subtask for recommending items to users. In the curation of citation databases, it can be used for record deduplication. In bioinformatics, it has been used to predict protein-protein interactions (PPI). It is also used to identify hidden groups of terrorists and criminals in security related applications.
A Graph neural network (GNN) is a class of artificial neural networks for processing data that can be represented as graphs.
References
- ↑ Hamilton, Willian L.; Ying, Rex; Leskovec, Jure (2017). "Representation learning on graphs: Methods and applications". IEEE Data Engineering Bulletin: 1. arXiv: 1709.05584 .
- ↑ "Deep Learning on Graphs, Chapter 4 Graph Embedding" (PDF).
- ↑ Colyer, Adrian (2017). "Struc2vec: learning node representations from structural identity". The Morning Paper.
- ↑ Ribeiro, Leonardo F. R.; Savarese, Pedro H. P.; Figueiredo, Daniel R. (2017). "struc2vec: Learning Node Representations from Structural Identity". KDD: Proceedings. International Conference on Knowledge Discovery & Data Mining. 2017: 385–394. arXiv: 1704.03165 . doi:10.1145/3097983.3098061. S2CID 3948366.