dot-compute_degree_matrix: GraphZoom: Faithful Implementation with RcppHNSW-based kNN
In bbuchsbaum/graphweights: Constructing Adjacency Matrices Based on Spatial and Feature Similarity
.compute_degree_matrix R Documentation
GraphZoom: Faithful Implementation with RcppHNSW-based kNN
Description
This code implements the GraphZoom framework as per the given paper:
"GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding"
Usage
.compute_degree_matrix(A)
Details
Added Feature:
- You can now provide a precomputed feature graph (A_feat). If provided, it will be used directly.
Otherwise, if X is provided, it will construct the feature graph from X. If neither is provided,
it will rely solely on the topology graph.
The four phases are:
1) Graph Fusion (Sec. 3.1)
2) Spectral Graph Coarsening (Sec. 3.2)
3) Graph Embedding on the Coarsest Graph (Sec. 3.3)
4) Embedding Refinement (Sec. 3.4)
References
GraphZoom paper; RcppHNSW documentation
bbuchsbaum/graphweights documentation built on Dec. 14, 2024, 1:13 a.m.
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| .compute_degree_matrix | R Documentation |
GraphZoom: Faithful Implementation with RcppHNSW-based kNN
Description
This code implements the GraphZoom framework as per the given paper: "GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding"
Usage
.compute_degree_matrix(A)
Details
Added Feature: - You can now provide a precomputed feature graph (A_feat). If provided, it will be used directly. Otherwise, if X is provided, it will construct the feature graph from X. If neither is provided, it will rely solely on the topology graph.
The four phases are: 1) Graph Fusion (Sec. 3.1) 2) Spectral Graph Coarsening (Sec. 3.2) 3) Graph Embedding on the Coarsest Graph (Sec. 3.3) 4) Embedding Refinement (Sec. 3.4)
References
GraphZoom paper; RcppHNSW documentation
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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