AHCKA: Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation (SIGMOD 2023)

Link to our paper

If you use our code or data, please cite:

@article{LiYS23,
  author       = {Yiran Li and
                  Renchi Yang and
                  Jieming Shi},
  title        = {Efficient and Effective Attributed Hypergraph Clustering via K-Nearest
                  Neighbor Augmentation},
  journal      = {Proc. {ACM} Manag. Data},
  volume       = {1},
  number       = {2},
  pages        = {116:1--116:23},
  year         = {2023}
}

This repository contains the implementation of AHCKA algorithm for attributed hypergraph clustering and the eight datasets used in our experiments.

Pre-requisites

numpy, scipy, scikit-learn, scann

Install with: pip install {library_name}

Optional: large-scale datasets

Processed Amazon and MAG-PM datasets, with constructed ScaNN indices, can be downloaded at: https://zenodo.org/records/10035761

Original Amazon and MAG data can be accessed via the following links.

Amazon: https://nijianmo.github.io/amazon/index.html

MAG (OAG v2): https://www.aminer.org/oag2019

Usage

To run AHCKA clustering algorithm on a dataset, please specify the type of dataset by command-line parameter --data and its name by --dataset. Datasets supported by our implementation include the following:

Type of dataset	--data	--dataset
Co-authorship hypergraph in .pickle files	coauthorship	cora, dblp
Co-citation hypergraph in .pickle files	cocitation	cora, citeseer
Attributed hypergraph stored in .npz file	npz	query, 20news, amazon, magpm

Other parameters are optional:

Parameter	Default	Description
--knn_k	10	$K$, the size of neighborhood in KNN graph
--alpha	0.2	$\alpha$, restart probability in random walk with restart (RWR)
--beta	0.5	$\beta$, the weight of KNN random walk
--tmax	200	$T_{a}$, the maximum number of orthogonal iterations
--interval	5	$\tau$, the interval of computing discrete cluster labels
--scale	-	Apply settings for large-scale data: approximate KNN with ScaNN; simplified initialization ($T_i$=1)
--rd_init	-	Initialize cluster labels at random
--seeds	0	Random seed, only effective with --rd_init on
--verbose	-	Produce verbose command-line output

To reproduce the results in our paper, please use the following commands for corresponding datasets.

Query

python AHCKA.py --dataset query --data npz

Cora-CA

python AHCKA.py --dataset cora --data coauthorship

Cora-CC

python AHCKA.py --dataset cora --data cocitation

Citeseer

python AHCKA.py --dataset citeseer --data cocitation

20News

python AHCKA.py --dataset 20news --data npz

DBLP

python AHCKA.py --dataset dblp --data coauthorship

Note: the following tests require downloading optional large-scale datasets with constructed ScaNN indices.

Amazon

python AHCKA.py --dataset amazon --data npz --scale --beta 0.4 --interval 1

MAG-PM

python AHCKA.py --dataset magpm --data npz --scale --beta 0.4

Sample output of AHCKA, on Cora co-authorship dataset:

Acc=0.651 F1=0.608 NMI=0.462 ARI=0.406 Time=0.470s RAM=221MB