Active Learning for Node Classification using a Convex Optimization approach

The recent advancements related to big data analytics in the era of Industry 4.0 are fueled by development and deployment of decision models based on neural network (NN) architectures. In addition to the data represented in Euclidean space, like, images, text, or videos, there are increasing applications which demand data representation in non-Euclidean domains. Such data are typically represented as graphs with complex interactions and interdependencies between various entities. The complexity of graph data imposes substantial challenge on the traditional Deep NN models, and Graph Neural Networks (GNN) are a powerful extension for modeling and analysis of networked data. The training of these computational models requires large amounts of labeled data. Active Learning (AL) helps to overcome this issue by selecting the most informative instances for labeling during the training process. This paper combines AL with GNN for semi-supervised classification of nodes in attributed graphs. The AL framework is portrayed as a convex optimization problem by employing Dissimilarity-based Sparse Modeling Representative Selection (DSMRS). The experimental evaluation demonstrates that using the selected graph-specific metrics (centrality and robustness measures) as AL heuristics leads to an improvement in classification performance by upto 10%.