Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction
This work addresses classification errors in graph-based domains with diverse topics, though it appears incremental as it builds on existing GCN approaches.
The paper tackles the challenge of extracting high-level features from graphs with complex connections by proposing a multi-stage non-deterministic classification method using secondary concept graphs and graph convolutional networks, achieving accuracies of 96%, 93%, and 95% on Cora, Citeseer, and PubMed datasets.
Graphs, comprising nodes and edges, visually depict relationships and structures, posing challenges in extracting high-level features due to their intricate connections. Multiple connections introduce complexities in discovering patterns, where node weights may affect some features more than others. In domains with diverse topics, graph representations illustrate interrelations among features. Pattern discovery within graphs is recognized as NP-hard. Graph Convolutional Networks (GCNs) are a prominent deep learning approach for acquiring meaningful representations by leveraging node connectivity and characteristics. Despite achievements, predicting and assigning 9 deterministic classes often involves errors. To address this challenge, we present a multi-stage non-deterministic classification method based on a secondary conceptual graph and graph convolutional networks, which includes distinct steps: 1) leveraging GCN for the extraction and generation of 12 high-level features: 2) employing incomplete, non-deterministic models for feature extraction, conducted before reaching a definitive prediction: and 3) formulating definitive forecasts grounded in conceptual (logical) graphs. The empirical findings indicate that our proposed approach outperforms contemporary methods in classification tasks. Across three datasets Cora, Citeseer, and PubMed the achieved accuracies are 96%, 93%, and 95%, respectively. Code is available at https://github.com/MasoudKargar.