Summary of Hyperdimensional Representation Learning For Node Classification and Link Prediction, by Abhishek Dalvi et al.
Hyperdimensional Representation Learning for Node Classification and Link Prediction
by Abhishek Dalvi, Vasant Honavar
First submitted to arxiv on: 26 Feb 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 HDGL, a novel method for node classification and link prediction in graphs, maps node features into a high-dimensional space using the injectivity property of node representations in Graph Neural Networks (GNNs). HDGL then aggregates local neighborhood information through bundling and binding, yielding latent node representations suitable for both tasks. Unlike GNNs requiring iterative optimization and hyperparameter tuning, HDGL requires only a single pass through the dataset. Experimenting on benchmark datasets, HDGL achieves competitive accuracy with state-of-the-art GNN methods at reduced computational cost for node classification, and matches DeepWalk’s performance on link prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HDGL is a new way to analyze graphs, like social networks or internet connections. It helps computers learn about nodes (like people or websites) and how they’re connected. HDGL works by looking at each node’s features and then combining that information with what’s happening around it. This helps the computer make good predictions about which nodes are likely to be friends or have a connection. The best part is that HDGL only needs to look at the data once, unlike other methods that need to repeat this process many times. HDGL works well on popular datasets and could be useful for lots of real-world problems. |
Keywords
* Artificial intelligence * Classification * Gnn * Hyperparameter * Optimization
