Link Weight Prediction with Node Embeddings

Abstract: Application of deep learning has been successful in various domains such as im-
      age 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 differ-
      ent node embedding techniques experimentally and compare its performance with
      two state-of-the-art non deep learning baseline approaches. Our experiment re-
      sults 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.

Reviews and author responses:

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.

The authors propose to use pretrained node embeddings in a deep learning model for link weight prediction in graphs. 
The embedding of the source node and the destination node are concatenated and fed into a fully connected neural network which produces the link weight as its output.
Existing work by Hou and Holder 2017 trains the same architecture, but the node embeddings are learned together with the weights of the neural network. In my professional opinion, the idea of using pretrained node embeddings and training only the neural network is not enough of a contribution.

Since the proposed method does not build on the SBM or pWSBM the detailed equations on page 2 are not necessary. Also, Figure 1, 2, and 3 are not necessary. Fully connected neural networks are widely used and can be explained briefly without drawing the architecture. 

Pros:
+ interesting problem
+ future work. evaluation of embeddings is indeed a hard problem worth solving.

Cons:
- not novel

The paper presents a generic approach to graph link weight prediction problems based on node enbeddings. After introducing several existing methods, the paper proposes a "generic" link weight prediction approach that uses the node embedding produced by any node embedding techniques. Six datasets are used for evaluation. 

Overall, the difference to the existing method [1] is minor. I don't think there is much novelty in the "generic" approach. More essential abstraction and comprehensive analysis is needed for a strong ICLR paper. 

[1] Yuchen Hou and Lawrence B Holder. Deep learning approach to link weight prediction. In Neural
Networks (IJCNN), 2017 International Joint Conference on, pp. 1855–1862. IEEE, 2017.



Meta-review: This paper does not meet the acceptance bar this year, and thus I must recommend it for rejection.

Decision: rejected-papers