Summary of Graphviz2vec: a Structure-aware Feature Generation Model to Improve Classification in Gnns, by Shraban Kumar Chatterjee et al.
GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs
by Shraban Kumar Chatterjee, Suman Kundu
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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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 introduces GraphViz2Vec, a novel feature extraction methodology that captures structural information in graph neural networks (GNNs). Traditional GNN architectures assume random or distribution-based initial embeddings, which require multiple layers to converge. However, this approach can lead to over-smoothing and neglects local neighbourhood structures. The proposed method generates meaningful initial embeddings for GNN models by leveraging image classification models trained on energy diagrams of a node’s local neighbourhood. These embeddings improve the performance of existing models in node and link classification tasks, with some achieving state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are powerful tools that help computers understand relationships between things. They’re really good at finding patterns in data, but sometimes they need a little help to get started. This paper introduces a new way to give GNNs a boost by using pictures of relationships between nodes. By looking at these pictures, the computer can create better starting points for its calculations, which makes it better at classifying things. The result is more accurate predictions and a clearer understanding of how things are connected. |
Keywords
* Artificial intelligence * Classification * Feature extraction * Gnn * Image classification
