HyperSearch: Prediction of New Hyperedges through Unconstrained yet Efficient Search (ICDM'25)

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.

Datasets

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
Email	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	✔

Execution

To execute HyperSearch, run this command:

mvn compile -B
mvn exec:java -Dexec.args="dataset name"
mvn exec:java -Dexec.args="citeseer"

Observations

To get the results from the observations in real-world hypergraphs, run this command:

python observation_1.py
python observation_2.py

Reference

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.