# Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2403.16033 · HTML: https://mlsanity.com/paper/2403.16033/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [Meta-GNN: On Few-shot Node Classification in Graph Meta-learning](https://mlsanity.com/paper/1905.09718/) (2019)
- [Dynamic Graph Convolutional Networks](https://mlsanity.com/paper/1704.06199/) (2017)
- [Graph-MLP: Node Classification without Message Passing in Graph](https://mlsanity.com/paper/2106.04051/) (2021)
- [GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs](https://mlsanity.com/paper/2012.04187/) (2020)
- [Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation](https://mlsanity.com/paper/2203.16097/) (2022)
- [Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection](https://mlsanity.com/paper/2005.07886/) (2020)

## Built on by (most cited first, 2 of 2)

- [Unifying Ontology Construction and Semantic Alignment for Deterministic Enterprise Reasoning at Scale](https://mlsanity.com/paper/2604.09608/) (2026)
- [From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI](https://mlsanity.com/paper/2604.08603/) (2026)

## Abstract (arXiv)

Graph data, also known as complex network data, is omnipresent across various domains and applications. Prior graph neural network models primarily focused on extracting task-specific structural features through supervised learning objectives, but they fell short in capturing the inherent semantic and structural features of the entire graph. In this paper, we introduce the semantic-structural attention-enhanced graph convolutional network (SSA-GCN), which not only models the graph structure but also extracts generalized unsupervised features to enhance vertex classification performance. The SSA-GCN's key contributions lie in three aspects: firstly, it derives semantic information through unsupervised feature extraction from a knowledge graph perspective; secondly, it obtains structural information through unsupervised feature extraction from a complex network perspective; and finally, it integrates these features through a cross-attention mechanism. By leveraging these features, we augment the graph convolutional network, thereby enhancing the model's generalization capabilities. Our experiments on the Cora and CiteSeer datasets demonstrate the performance improvements achieved by our proposed method. Furthermore, our approach also exhibits excellent accuracy under privacy settings, making it a robust and effective solution for graph data analysis.