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. HPLC combines landmark selection and graph clustering%2C where the graph is partitioned into densely connected clusters in which nodes with the highest degree are selected as landmarks. HPLC leverages the positional information of nodes based on landmarks at various levels of hierarchy such as nodes' distances to landmarks%2C inter-landmark distances and hierarchical grouping of clusters. Experiments show that HPLC achieves state-of-the-art performances of link prediction on various datasets in terms of HIT%40K%2C MRR%2C and AUC. The code is available at url{https:%2F%2Fgithub.com%2Fkmswin1%2FHPLC}.?quality=80&w=800)
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- Hierarchical Position Embedding of Graphs with Landmarks and Clustering ...
- Hierarchical Position Embedding of Graphs with Landmarks and Clustering ...
- Hierarchical Position Embedding of Graphs with Landmarks and Clustering ...
- GitHub - pradeepsuyal/Hierarchical-Clustering
- Hierarchical clustering of landmarks | Download Scientific Diagram
- Hierarchical clustering of landmarks | Download Scientific Diagram
- Hierarchical clustering of landmarks | Download Scientific Diagram
- Table 1 from Hierarchical Position Embedding of Graphs with Landmarks ...
- a) shows the conventional approach to hierarchical graphs, which is ...
- Table 3 from Hierarchical Position Embedding of Graphs with Landmarks ...