[Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning (Under review at IEEE TKDE)(https://arxiv.org/abs/2309.05798)

Authors: Yunyong Ko, Hanghang Tong, and Sang-Wook Kim.
This repository provides an implementation of CASH.

The overview of CASH

• Important Challenges
  • We point out two important but under-explored challenges of hyperedge prediction: (C1) the node aggregation of a hyperedge candidate and (C2) the data sparsity.
• Effective Framework
  • We propose a novel hyperedge prediction framework, CASH that employs (1) a context-aware node aggregation for C1 and (2) self-supervised learning equipped with hyperedge-aware augmentation and dual contrastive loss for C2.
• Extensive Evaluation
  • We conduct extensive evaluation, which verifies the superiority of CASH in terms of (1) accuracy, (2) effectiveness, (3) insensitivity, and (4) scalability.

Datasets

All datasets used in the paper are available at Link.

Name	#Nodes	#Edges	#Features	Domain
Citeseer	1,457	1,078	3703	Co-citation
Cora	1,434	1,579	1433	Co-citation
Pubmed	3,840	7,962	500	Co-citation
Cora-A	2,388	1,072	1433	Authorship
DBLP-A	39,283	16,483	4543	Authorship
DBLP	2,388	22,964	4543	Collaboration

Dataset format

Each dataset file contains following keys: 'N_edges', 'N_nodes', 'NodeEdgePair', 'EdgeNodePair', 'nodewt', 'edgewt', 'node_feat'. We also provide preprocessed splits, each of which contains train, validation, and test sets (both positive and negative).

Dependencies

Our code runs on the Intel i7-9700k CPU with 64GB memory and NVIDIA RTX 2080 Ti GPU with 12GB, with the following packages installed:

python 3.8.10
torch 1.11.0
dgl 0.9.0
numpy
argparse
sklearn

How to run (example)

python3 -u run.py \
    --gpu_index=0 \
    --batch_size=32 \
    --ns_method=SNS \
    --h_dim=512 \
    --proj_dim=512 \
    --drop_feature_rate=0.24 \
    --drop_incidence_rate=0.4 \
    --num_epochs=200 \
    --dataset=citeseer \
    --learning_rate=0.005 \