Main Article Content
Link classification categorises links between nodes of graphs for improved graph learning.
This work proposes a novel approach of using the frequency of transactions between nodes to learn
affinity for associations and thereby classifies links between nodes. Further, the classification is done
for multiple grades of classification and not just as strong/weak links. The model is successfully able
to classify links with around 95 percent micro-F1 accuracy on both homogeneous and heterogeneous
datasets using a multi-layer perceptron network.
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