jianliu91 / renet

Recurrent Event Network for Reasoning over Temporal Knowledge Graphs

PyTorch implementation of Recurrent Event Network (RE-Net)

Paper: Recurrent Event Network for Reasoning over Temporal Knowledge Graph

ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.

Installation

Install PyTorch (>= 0.4.0) following the instuctions on the official website. Our code is written on Python3.

Train and Test

Before running, you should preprocess datasets.

python3 data/DATA_NAME/get_history.py

Then, we are ready to train and test. We first train the model.

python3 train.py -d DATA_NAME --gpu 0 --model 0 --dropout 0.5 --n-hidden 200 --lr 1e-3 --max-epochs 20 --batch-size 1024

We are ready to test!

python3 test.py -d DATA_NAME --gpu 0 --model 0 --n-hidden 200

The default hyperparameters give the best performances.

Model variants

The user must specify a --model, the variants of which are described in detail in the paper:

• Attentive aggregator: --model 0
• Mean aggregator: --model 1
• GCN aggregator: --model 2

Related Work

There are related literatures: Temporal Knowledge Graph Embedding, Dynamic Graph Embedding, Knowledge Graph Embedding, Static Graph Embedding, etc. We organized the list of related work.

Datasets

There are two datasets: ICEWS18, GDELT. Each data folder has 'stat.txt', 'train.txt', 'valid.txt', 'test.txt', and 'get_history.py'.

• 'get_history': This is for getting history for each entity.
• 'stat.txt': First value is the number of entities, and second value is the number of relations.
• 'train.txt', 'valid.txt', 'test.txt': First column is subject entities, second column is relations, and third column is object entities. The fourth column is time.

Baselines

We use the following public codes for baselines and hyperparameters. We validated embedding sizes among presented values.

Baselines	Code	Embedding size	Batch size
TransE	Link	100, 200	1024
DistMult	Link	100, 200	1024
ComplEx	Link	50, 100, 200	100
RGCN	Link	200	Default
ConvE	Link	200	128
Know-Evolve	Link	Default	Default
HyTE	Link	128	Default

We implemented TA-TransE, TA-DistMult, and TTransE. The user can find implementations in the 'baselines' folder.

Predictive performances

In the ICEWS18 dataset, the results with filtered metrics:

Method	MRR	Hits@1	Hits@3	Hits@10
RE-Net (mean)	42.38	35.80	44.99	54.90
RE-Net (Attn)	41.46	34.67	44.19	54.44
RE-Net (GCN)	41.35	34.53	44.05	54.35

In the GDELT dataset, the results with filtered metrics:

Method	MRR	Hits@1	Hits@3	Hits@10
RE-Net (mean)	39.15	30.84	43.07	53.48
RE-Net (Attn)	38.07	29.44	42.26	52.93
RE-Net (GCN)	37.99	30.05	41.40	52.18