Implemetation of "MCNS": Understanding Negative Sampling in Graph Representation Learning.

Understanding Negative Sampling in Graph Representation Learning.

Zhen Yang*, Ming Ding*, Chang Zhou, Hongxia Yang, Jingren Zhou, Jie Tang. (*These authors contributed equally to this work.)

In KDD 2020 (Research Track)

We systematically analyze the role of negative sampling from the perspectives of both objective and risk, and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution. With the guidance of the theory, we propose MCNS, approximating the positive distribution with self-contrast approximation and accelerating negative sampling by Metropolis-Hastings.

• Python 3.7
• Tensorflow 1.14.0

You can use $ ./experiments/***.sh to train MCNS model on the recommendation task. For example, if you want to train on the Amazon dataset, you can run $ ./experiments/amazon.sh or python main.py --input data/amazon/ to train MCNS model.

if you want to train MCNS on your own dataset, you should prepare the following four files:

• train.txt: Each line represents an edge <node1> <node2>.
• valid.txt: the same format with train.txt
• test.txt: the same format with train.txt
• test_neg.txt: For each node, we select some unconnected nodes as negs for evaluation. For Amazon and Alibaba datasets, we select 500 negs, and all unconnected negs for ml-100k to evaluate hits@k and MRR. (generated by load_data.py/load_test_neg function).

• ml-100k contains 943 users, 1,682 items and 100,000 edges.
• Amazon contains 192,403 users, 63,001 items and 1,689,188 edges.
• Alibaba contains 106,042 users, 53591 items and 907,470 edges.

The trainable encoder of our code is based on GraphSAGE.