Summary of Tackling Oversmoothing in Gnn Via Graph Sparsification: a Truss-based Approach, by Tanvir Hossain et al.
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach
by Tanvir Hossain, Khaled Mohammed Saifuddin, Muhammad Ifte Khairul Islam, Farhan Tanvir, Esra Akbas
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to address the oversmoothing problem in Graph Neural Networks (GNNs) by introducing a truss-based graph sparsification model. The oversmoothing issue arises when repeated aggregation operations lead to excessive mixing of node representations, resulting in nearly indistinguishable embeddings. To overcome this challenge, the proposed model prunes edges from dense regions of the graph, preventing the aggregation of excessive neighborhood information during hierarchical message passing and pooling in GNN models. The approach is demonstrated on various real-world datasets and state-of-the-art baseline GNN models, including GIN, SAGPool, GMT, DiffPool, MinCutPool, HGP-SL, DMonPool, and AdamGNN. The results show significant improvements in the graph classification task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with Graph Neural Networks that makes it hard to get accurate results from some types of data. When GNNs try to analyze big networks like social media or biological networks, they can get overwhelmed by too much information and start to blend together similar patterns. This makes it harder for the network to learn important features. The researchers propose a new way to make GNNs work better by removing unnecessary connections in the data. They test this approach on different datasets and show that it improves performance. |
Keywords
* Artificial intelligence * Classification * Gnn
