This critique will be based on two papers listed below on unsupervised graph representation learning. Your critique should be no more than 1500 words. Please submit your critique as a PDF via Canvas. The papers are:
- node2vec: Scalable Feature Learning for Networks
- OhmNet: Predicting multicellular function through multi-layer tissue networks
Your critique should cover each of the following points. Please dedicate at least one section to each of these points.
1. What is the general problem area these papers are addressing?
2. Briefly describe the motivation of the method described in each paper and how the method accomplishes its designated goal.
3. What novel results and insights were obtained using these methods?
4. Both papers are based on the node2vec algorithm. Compared to other unsupervised representation learning approaches on graphs, what are some of the key strengths and weaknesses of node2vec?