We describe a method to select the nodes in Graph datasets for training so that the model trained on the points selected will be be better than the ones if we select other points for the purpose of training . This is a very important aspect as the process of labelling the points is often a costly affair. The usual Active Learning methods are good but the penalty involved with these methods is that, we need to re-train the model after selecting the nodes in each iteration of Active Learning cycle. We come up with a method which use the concept of Graph Centrality to select the nodes for labeling and training initially and the training is needed to perform only once . We have tested this idea on three  graph datasets - Cora, Citeseer and Pubmed- and the results are really encouraging.
The process of selecting points for training a machine learning model is often a challenging task. Many times, we will have a lot of data, but for training, we require the labels and labeling is often costly. So we need to select the points for training in an efficient manner so that the model trained on the points selected will be better than the ones trained on any other training set. We propose a novel method to select the nodes in graph datasets using the concept of graph centrality. Two methods are proposed - one using a smart selection strategy, where the model is required to be trained only once and another using active learning method . We have tested this idea on three popular graph datasets - Cora, Citeseer and Pubmed- and the results are found to be encouraging.
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We describe a method
The process of selecting points for training a machine learning model is often a challenging task. Many times, we will have a lot of data, but for training, we require the labels and labeling is often costly. So we need
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nodes in Graph datasets for training
points for training in an efficient manner
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be
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if we select other points for the purpose of training
trained on any other training set. We propose a novel method to select the nodes in graph datasets using the concept of graph centrality. Two methods are proposed - one using a smart selection strategy, where the model is required to be trained only once and another using active learning method
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This is a very important aspect as the process of labelling the points is often a costly affair. The usual Active Learning methods are good but the penalty involved with these methods is that, we need to re-train the model after selecting the nodes in each iteration of Active Learning cycle. We come up with a method which use the concept of Graph Centrality to select the nodes for labeling and training initially and the training is needed to perform only once .
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popular
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really
found to be