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  2. Graph neural network - Wikipedia

    en.wikipedia.org/wiki/Graph_neural_network

    The graph convolutional network (GCN) was first introduced by Thomas Kipf and Max Welling in 2017. [9] A GCN layer defines a first-order approximation of a localized spectral filter on graphs. GCNs can be understood as a generalization of convolutional neural networks to graph-structured data. The formal expression of a GCN layer reads as follows:

  3. Graph database - Wikipedia

    en.wikipedia.org/wiki/Graph_database

    Despite the graph databases' advantages and recent popularity over [citation needed] relational databases, it is recommended the graph model itself should not be the sole reason to replace an existing relational database. A graph database may become relevant if there is an evidence for performance improvement by orders of magnitude and lower ...

  4. Knowledge graph - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph

    In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised. These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like word embeddings. This can complement ...

  5. Knowledge graph embedding - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph_embedding

    The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. [1] [3] [5] [6] [7] [18] Rossi et al. [5] produced an extensive benchmark of the models, but also other surveys produces similar results.

  6. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection: 2016 (possibly updated with new datasets and/or results) [332] Campos et al.

  7. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).

  8. List of datasets in computer vision and image processing

    en.wikipedia.org/wiki/List_of_datasets_in...

    This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.

  9. Link prediction - Wikipedia

    en.wikipedia.org/wiki/Link_prediction

    The task of link prediction has attracted attention from several research communities ranging from statistics and network science to machine learning and data mining.In statistics, generative random graph models such as stochastic block models propose an approach to generate links between nodes in a random graph.