Datasets:

daqh
/

NDC-classes

@@ -53,7 +53,7 @@ The hypergraph is stored in HIF (Hypergraph Interchange Format) as a JSON object
 In addition to the raw hypergraph topology, vector features are provided for both nodes and hyperedges (in their attribute dictionaries), enabling out-of-the-box experimentation with representation learning and downstream tasks:
 - Spectral features: eigenvectors of the (hypergraph) Laplacian (computed via sparse eigensolvers).
-- [Node2Vec](https://dl.acm.org/doi/10.1145/2939672.2939754) embeddings: random-walk–based structural embeddings.
 - [VilLain](https://dl.acm.org/doi/10.1145/3589334.3645454) embeddings: self-supervised hypergraph representation learning via virtual label propagation.
 Basic statistics (as packaged here): 327 nodes, 12,799 hyperedges, 1 connected component.

 In addition to the raw hypergraph topology, vector features are provided for both nodes and hyperedges (in their attribute dictionaries), enabling out-of-the-box experimentation with representation learning and downstream tasks:
 - Spectral features: eigenvectors of the (hypergraph) Laplacian (computed via sparse eigensolvers).
+- [Node2Vec](https://arxiv.org/abs/1607.00653) embeddings: random-walk–based structural embeddings.
 - [VilLain](https://dl.acm.org/doi/10.1145/3589334.3645454) embeddings: self-supervised hypergraph representation learning via virtual label propagation.
 Basic statistics (as packaged here): 327 nodes, 12,799 hyperedges, 1 connected component.