Application of deep learning has been successful in various domains such as image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present the first generic deep learning approach to the graph link weight prediction problem based on node embeddings. We evaluate this approach with three different node embedding techniques experimentally and compare its performance with two state-of-the-art non deep learning baseline approaches. Our experiment results suggest that this deep learning approach outperforms the baselines by up to 70% depending on the dataset and embedding technique applied. This approach shows that deep learning can be successfully applied to link weight prediction to improve prediction accuracy.
Although this paper aims at an interesting and important task, the reviewer does not feel it is ready to be published.
Below are some detailed comments:
Pros
- Numerous public datasets are used for the experiments
- Good introductions for some of the existing methods.
Cons
- The novelty is limited. The basic idea of the proposed method is to simply concatenate the embeddings of two nodes (via activation separately) from both side of edges, which is straightforward and produces only marginal improvement over existing methods (the comparison of Figure 1 and Figure 3 would suggest this fact). The optimization algorithm is not novel either.
- Lack of detailed description and analysis for the proposed model S. In Section 5.2, only brief descriptions are given for the proposed approach.
- The selected baseline methods are too weak as competitors, some important relevant methods are also missing in the comparisons. For the graph embedding learning task, one of the state-of-the-art approach is conducting Graph Convolutional Networks (GCNs), and GCNs seem to be able to tackle this problem as well. Moreover, the target task of this paper is mathematically identical to the rating prediction problem (if we treat the weight matrix of the graph as the rating matrix, and consider the nodes as users, for example), which can be loved by a classic collaborative filtering solution such as matrix factorization. The authors probably need to survey and compared against the proposed approach.