Duplicate from daqh/NDC-classes

Co-authored-by: Valerio Di Pasquale <daqh@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- en
+tags:
+- hypergraph
+task_categories:
+- graph-ml
+---
+# NDC-classes
+
+[Zenodo](https://zenodo.org/records/10155772) | [Cornell](https://www.cs.cornell.edu/~arb/data/NDC-classes/) | [Source Paper](https://arxiv.org/abs/1802.06916)
+
+NDC-classes is an undirected hypergraph built from the U.S. FDA’s National Drug Code (NDC) Directory, designed for higher-order network / hypergraph machine learning in the drug domain. Each hyperedge corresponds to a drug and connects the set of pharmacologic/therapeutic class labels assigned to that drug, while nodes represent the class labels themselves (e.g., “serotonin reuptake inhibitor”), capturing co-classification patterns as higher-order interactions rather than pairwise links.
+
+## Usage
+
+```python
+dataset = load_dataset("NDC-classes", split="full")
+hypergraphs = [xgi.from_hif_dict(d, nodetype=int, edgetype=int) for d in dataset]
+```
+
+## Statistics
+
+<div align="center">
+  <table>
+    <tbody>
+      <tr>
+        <td colspan="2" align="center">
+          <figure>
+            <img src="assets/hypergraph.png">
+          </figure>
+        </td>
+      </tr>
+      <tr>
+        <td align="center">
+          <figure>
+            <img src="assets/node-degree-distribution.png">
+          </figure>
+        </td>
+        <td align="center">
+          <figure>
+            <img src="assets/hyperedge-size-distribution.png">
+          </figure>
+        </td>
+      </tr>
+    </tbody>
+  </table>
+</div>
+
+## Content
+
+The hypergraph is stored in HIF (Hypergraph Interchange Format) as a JSON object, following the schema used to exchange higher-order network data across tools. Concretely, the dataset provides the canonical HIF fields-network-type, metadata, nodes, edges, and incidences-so you can reconstruct the full incidence structure without additional processing.
+
+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): 1161 nodes, 1088 hyperedges, 183 connected component, largest connected component ratio is 0.540913006029285.
+
+## Citation
+
+```
+@article{Benson-2018-simplicial,
+ author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon},
+ title = {Simplicial closure and higher-order link prediction},
+ year = {2018},
+ doi = {10.1073/pnas.1800683115},
+ publisher = {National Academy of Sciences},
+ issn = {0027-8424},
+ journal = {Proceedings of the National Academy of Sciences}
+}
+```

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