This repository contains the source code for the paper HyperSearch: Prediction of New Hyperedges through Unconstrained yet Efficient Search by Hyunjin Choo, Fanchen Bu, Hyunjin Hwang, Young-Gyu Yoon, and Kijung Shin, to be presented at ICDM 2025.
In this work, we propose HyperSearch, a search-based algorithm for hyperedge prediction that efficiently evaluates unconstrained candidate sets, by incorporating two key components:
- Empirically justified scores based on observations: An empirically grounded scoring function derived from observations in real-world hypergraphs.
- Efficient search with an anti-monotonic upper bound: We derive and use an anti-monotonic upper bound of the original scoring function (which is not anti-monotonic) to prune the search space.
All datasets are available at this link and this link.
| Domain | Dataset | # Nodes | # Hyperedges | Timestamps |
|---|---|---|---|---|
| Co-citation | Citeseer | 1,457 | 1,078 | |
| Cora | 1,434 | 1,579 | ||
| Authorship | Cora-A | 2,388 | 1,072 | |
| DBLP-A | 39,283 | 16,483 | ||
| Enron | 143 | 10,883 | ✔ | |
| Eu | 998 | 234,760 | ✔ | |
| Contact | High | 327 | 172,035 | ✔ |
| Primary | 242 | 106,879 | ✔ | |
| Tags | math.sx | 1,629 | 822,059 | ✔ |
| ubuntu | 3,029 | 271,233 | ✔ |
To execute HyperSearch, run this command:
mvn compile -B
mvn exec:java -Dexec.args="dataset name"
mvn exec:java -Dexec.args="citeseer"
To get the results from the observations in real-world hypergraphs, run this command:
python observation_1.py
python observation_2.py
This code is free and open source for only academic/research purposes (non-commercial). If you use this code as part of any published research, please acknowledge the following paper.
