This repository contains the ANCKA clustering framework for general attributed networks, extended from the previously published AHCKA algorithm for attributed hypergraph:
@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}
}The following attributed network datasets used in this work are also made available:
-
Attributed undirected/directed graphs: Cora, Undirected Citeseer, Wiki, Directed Citeseer, Tweibo*, Amazon2M*.
-
Attributed hypergraphs: Cora-CA, Cora-CC, Query, Citeseer, 20News, DBLP, Amazon*, MAG-PM*.
-
Attributed multiplex graphs: ACM, DBLP, IMDB.
*Available at Zenodo upload due to space limit of GitHub repository.
-
Prepare Python environment with pip or conda:
-
Python version 3.9
-
Install required libraries: numpy, scipy, scikit-learn
-
-
(Optional) Extract attributed multiplex datasets:
unzip data/multi.zip -d data/
-
(Optional) To run ANCKA-GPU, install cupy and faiss-gpu=1.7.3.
-
(Optional) For testing large-scale datasets:
-
Install KNN search libraries: faiss-cpu and scann
-
Download datasets and KNN index files from Zenodo upload
-
Extract files:
cd ANCKA unzip ~/Download_path/ANCKA_data.zip -d data/
-
To run ANCKA clustering algorithm please specify the name of dataset by command-line parameter --dataset, and for hypergraph datasets also specify the type of dataset by --data. Besides, also provide the type of attributed network by --network_type. Datasets supported by our implementation include the following:
| Type of dataset | --data | --dataset | --network_type |
|---|---|---|---|
| Co-authorship hypergraph in .pickle files | coauthorship | cora, dblp | HG |
| Co-citation hypergraph in .pickle files | cocitation | cora, citeseer | HG |
| Attributed hypergraph stored in .npz file | npz | query, 20news, amazon, magpm | HG |
| Attributed undirected graph | - | cora,citeseer-UG,wiki,Amazon2M | UG |
| Attributed directed graph | - | citeseer-DG,Tweibo | DG |
| Attributed multiplex graph | - | ACM,IMDB,DBLP-MG | MG |
Other parameters are optional:
| Parameter | Default | Description |
|---|---|---|
| --knn_k | 10 |
|
| --alpha | 0.2 |
|
| --beta | 0.5 |
|
| --tmax | 200 |
|
| --interval | 5 |
|
| --scale | - | Apply settings for large-scale data: approximate KNN with ScaNN or Faiss; simplified initialization ( |
| --gpu | - | Use ANCKA-GPU implementation for clustering |
| --times | 10 | Rerun ANCKA-GPU algorithm to obtain average metrics |
| --caltime | - | Measure the time costs of different segments in ANCKA or ANCKA-GPU algorithm |
| --verbose | - | Produce verbose command-line output |
To reproduce the experiment results of ANCKA and ANCKA-GPU in our paper, please refer to command.sh for corresponding datasets.
Sample output of running bash command.sh:
CPU based
dataset:dblp data:coauthorship network_type:HG
parameter setting: k=10 init_iter=25 beta=0.5
ACC=0.797 F1=0.774 NMI=0.632 ARI=0.632 Time=30.899s
dataset:cora data:none network_type:UG
parameter setting: k=50 init_iter=25 beta=0.5
ACC=0.723 F1=0.686 NMI=0.556 ARI=0.484 Time=1.110s
dataset:citeseer data:none network_type:DG
parameter setting: k=50 init_iter=25 beta=0.4
ACC=0.696 F1=0.651 NMI=0.444 ARI=0.460 Time=1.152s
dataset:acm data:none network_type:MG
parameter setting: k=50 init_iter=25 beta=0.7
ACC=0.928 F1=0.928 NMI=0.739 ARI=0.796 Time=1.602s
GPU based
dataset:dblp data:coauthorship network_type:HG
parameter setting: k=10 init_iter=25 beta=0.5
ACC=0.808 F1=0.787 NMI=0.643 ARI=0.646 Time=0.663s
dataset:cora data:none network_type:UG
parameter setting: k=50 init_iter=25 beta=0.4
ACC=0.683 F1=0.621 NMI=0.533 ARI=0.470 Time=0.377s
dataset:citeseer data:none network_type:DG
parameter setting: k=50 init_iter=25 beta=0.4
ACC=0.694 F1=0.652 NMI=0.441 ARI=0.454 Time=0.839s
dataset:acm data:none network_type:MG
parameter setting: k=50 init_iter=25 beta=0.7
ACC=0.924 F1=0.924 NMI=0.730 ARI=0.786 Time=0.332s