NodeNet: A Graph Regularised Neural Network for Node Classification
This work addresses node classification for citation graphs, offering an incremental improvement by modifying existing graph-based techniques.
The authors tackled node classification in citation graphs by proposing NodeNet, a graph-regularized neural network based on Neural Graph Learning, which achieved superior performance compared to state-of-the-art methods.
Real-world events exhibit a high degree of interdependence and connections, and hence data points generated also inherit the linkages. However, the majority of AI/ML techniques leave out the linkages among data points. The recent surge of interest in graph-based AI/ML techniques is aimed to leverage the linkages. Graph-based learning algorithms utilize the data and related information effectively to build superior models. Neural Graph Learning (NGL) is one such technique that utilizes a traditional machine learning algorithm with a modified loss function to leverage the edges in the graph structure. In this paper, we propose a model using NGL - NodeNet, to solve node classification task for citation graphs. We discuss our modifications and their relevance to the task. We further compare our results with the current state of the art and investigate reasons for the superior performance of NodeNet.