Summary of Hierarchical Position Embedding Of Graphs with Landmarks and Clustering For Link Prediction, by Minsang Kim and Seungjun Baek
Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Prediction
by Minsang Kim, Seungjun Baek
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 The proposed method represents positional information in graphs using landmark nodes, which serve as reference points for node positions. A small number of high-degree centrality nodes are selected as landmarks, and their effectiveness is justified through theoretical bounds on average path lengths involving landmarks. The approach is applied to power-law graphs, where it provides asymptotically exact inter-node distance information. The Hierarchical Position embedding with Landmarks and Clustering (HPLC) method combines landmark selection and graph clustering to leverage positional information at various levels of hierarchy. Experimental results show that HPLC achieves state-of-the-art link prediction performance on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are used to represent complex relationships between nodes, but understanding the position of each node is important for tasks like link prediction. A new method uses special nodes called landmarks to help determine where other nodes are located in the graph. Landmarks are chosen based on how connected they are to other nodes, and this approach is shown to be effective through mathematical proofs. The Hierarchical Position embedding with Landmarks and Clustering (HPLC) method combines these landmark nodes with a clustering technique to provide even more accurate information about node positions. This helps improve link prediction performance on various datasets. |
Keywords
* Artificial intelligence * Clustering * Embedding
