# Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction

arXiv: https://arxiv.org/abs/2411.06212 · HTML: https://mlsanity.com/paper/2411.06212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Learning Graph Embedding with Adversarial Training Methods](https://mlsanity.com/paper/1901.01250/) (2019)
- [Graph-MLP: Node Classification without Message Passing in Graph](https://mlsanity.com/paper/2106.04051/) (2021)
- [Simplifying approach to Node Classification in Graph Neural Networks](https://mlsanity.com/paper/2111.06748/) (2021)
- [Semi-Supervised Graph Classification: A Hierarchical Graph Perspective](https://mlsanity.com/paper/1904.05003/) (2019)
- [Understanding graph embedding methods and their applications](https://mlsanity.com/paper/2012.08019/) (2020)
- [Accurate, Efficient and Scalable Graph Embedding](https://mlsanity.com/paper/1810.11899/) (2018)
- [Tree Decomposed Graph Neural Network](https://mlsanity.com/paper/2108.11022/) (2021)
- [Co-embedding of Nodes and Edges with Graph Neural Networks](https://mlsanity.com/paper/2010.13242/) (2020)
- [Towards Deep and Representation Learning for Talent Search at LinkedIn](https://mlsanity.com/paper/1809.06473/) (2018)
- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [A Simple Baseline Algorithm for Graph Classification](https://mlsanity.com/paper/1810.09155/) (2018)
- [AttrE2vec: Unsupervised Attributed Edge Representation Learning](https://mlsanity.com/paper/2012.14727/) (2020)
- [EGC2: Enhanced Graph Classification with Easy Graph Compression](https://mlsanity.com/paper/2107.07737/) (2021)
- [Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classification](https://mlsanity.com/paper/2402.09600/) (2024)

## Abstract (arXiv)

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.