A Non-constrastive loss for Unsupervised Graph Embeddings
We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph-structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide interesting insights into the graph structure, so that the separate node clustering step of existing methods is no longer needed. DMC learns both, node embeddings and communities, simultaneously by minimizing the mincut loss, which captures the number of connections between communities. Striving for high scalability, we also propose a training process for DMC based on minibatches. We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.
Although this paper seems to only combine existing techniques in community detection and node embedding into a co-train process. The idea is simple and easy understood and the paper is well-written. Theoretical analysis is provided for the approximation error for the sampling strategy. However, major concerns are:
1. Experimental results show that co-training node embedding and community detection can improve the performance for node classification. The improvements may result from the assumption that papers with the same class label are associated with the same community in the citation graph. However, in the dataset, there are many cases that there are not dense connections among the same labeled papers. The authors should check the correlation between the detected communities and the original paper labels.
2. No comparison with other community-preserving node embedding methods, such as "Community Preserving Network Embedding" in AAAI17
3. Since this paper aims to combine community detection and node embedding process, a set of baseline should be considered. For example, if considering the downstream node classification of node embedding as an evaluation task, then how about the performance of the following two-step method. We can first detect communities based on the node features then do graph node embedding by considering the communities' membership and node features together (e.g. simply concatenating both community membership features and node features).
4. Efficiency and scalability evaluations are needed. Spectral clustering has a scalability issue when meeting big graphs. Since the spectral process is also applied in the proposed method, efficiency and scalability evaluations are encouraged to provide, especially for big graphs which are not covered in the selected datasets in this paper.
5. In Sec 5.3 and Fig 2, it's mentioned that trends of the three datasets are different. For the increasing trend, how about the performance for an extreme case where all nodes are considered in one batch. On the other hand, adding more nodes in one minibatch could provide more information, but why there exists a decreasing trend? Though the authors provide a reason in Sec 5.3, it's better to analyze the reason directly from the datasets.