MV-HRE: Multi-View Heterogeneous Relation Embedding

This is the official code-repository for the ICDM'22 paper Revisiting Link Prediction on Heterogeneous Graphs with A Multi-view Perspective.

Motivation

MV-HRE is a multi-view network representation learning framework to incorporate structural intuitions and enrich the triplet representations for link prediction on heterogeneous graphs. MV-HRE incorporates Metapath-view and rarely studied Community-view for the task of link prediction in HINs besides local-contexts. It proposes first-of-its-kind Community-view for a triplet. It effectively aggregates multiple views at difference scales to provide enriched structural cues to predict links. Analysis of view importance suggests that all the chosen candidate views are indeed important and complementary in achieving the best performance on heterogeneous link prediction.

Execution

To run MV-HRE, execute the sample command:

python main.py --dataset <dataset> --clustering <0/1> --hidden_dim <hidden dimension> --lr <learning rate> --weight_decay <weight decay> --num_heads <number of heads> --num_layers <number of layers> --dropout <dropout> --context_hops <context_hops> --max_path_len <max_path_len> --path_samples <path_samples> --cluster_coeff <clustering coefficients> --num_clusters <number of clusters> --gpu_num <gpu_id>

Example: python main.py --dataset ddb --clustering 1 --hidden_dim 64 --lr 0.005 --weight_decay 0.001 --num_heads 2 --num_layers 4 --dropout 0.1 --context_hops 4 --max_path_len 5 --path_samples 5 --cluster_coeff 0.5 --num_clusters 25 --gpu 0