Graph Neural Network
Can graph machine learning identify hate speech in online social networks?
A use-case study for Twitter users and the power of machine learning and graph neural networks to detect hateful online users.
medium.com
-Graph Sample and Aggregate GraphSAGE
- GraphSAGE introduces a new type of graph convolutional neural network layer that propagates information from a node’s neighbourhood while training a classifier.
-Generally, graph neural network models can become computationally unwieldy for large graphs with high degree nodes. To avoid this, GraphSAGE employs a sampling scheme to limit the number of neighbours whose feature information is passed to the central node as shown in the “AGGREGATE” step in Figure 4.
- Furthermore, GraphSAGE models learn functions that can be used to generate latent representations for nodes that were not present in the network during training.
In consequence, GraphSAGE can suitably be used to make predictions in an inductive setting when only part of the graph is available at training time
stellargraph/stellargraph
StellarGraph - Machine Learning on Graphs. Contribute to stellargraph/stellargraph development by creating an account on GitHub.
github.com