Venue: ICLR 2017 conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: The paper introduces a method for semi-supervised learning in graphs that exploits the spectral structure of the graph in a convolutional NN implementation. The proposed algorithm has a limited complexity and it is shown to scale well on a large dataset. The comparison with baselines on different datasets show a clear jump of performance with the proposed method.

The paper is technically fine and clear, the algorithm seems to scale well, and the results on the different datasets compare very favorably with the different baselines. The algorithm is simple and training seems easy. Concerning the originality, the proposed algorithm is a simple adaptation of graph convolutional networks (ref Defferrard 2016 in the paper) to a semi-supervised transductive setting. This is clearly mentioned in the paper, but the authors could better highlight the differences and novelty wrt this reference paper. Also, there is no comparison with the family of iterative classifiers, which usually compare favorably, both in performance and training time, with regularization based approaches, although they are mostly used in inductive settings. Below are some references for this family of methods.

The authors mention that more complex filters could be learned by stacking layers but they limit their architecture to one hidden layer. They should comment on the interest of using more layers for graph classification.


Some references on iterative classification Qing Lu and Lise Getoor. 2003. Link-based classification. In ICML, Vol. 3. 496–503.

Gideon S Mann and Andrew McCallum. 2010. Generalized expectation criteria for semi-supervised learning with weakly labeled data. The Journal of Machine Learning Research 11 (2010), 955–984.
David Jensen, Jennifer Neville, and Brian Gallagher. 2004. Why collective inference improves relational classification. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 593–598.
Joseph J Pfeiffer III, Jennifer Neville, and Paul N Bennett. 2015. Overcoming Relational Learning Biases
to Accurately Predict Preferences in Large Scale Networks. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 853–
863.
Stephane Peters, Ludovic Denoyer, and Patrick Gallinari. 2010. Iterative annotation of multi-relational social networks. In Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on. IEEE, 96–103.

review_title: null

final_score: rating: 7: Good paper, accept
review_content: This paper proposes the graph convolutional networks, motivated from approximating graph convolutions.  In one propagation step, what the model does can be simplified as, first linearly transform the node representations for each node, and then multiply the transformed node representations with the normalized affinity matrix (with self-connections added), and then pass through nonlinearity.

This model is used for semi-supervised learning on graphs, and in the experiments it demonstrated quite impressive results compared to other baselines, outperforming them by a significant margin.  The evaluation of propagation model is also interesting, where different variants of the model and design decisions are evaluated and compared.

It is surprising that such a simple model works so much better than all the baselines.  Considering that the model used is just a two-layer model in most experiments, this is really surprising as a two-layer model is very local, and the output of a node can only be affected by nodes in a 2-hop neighborhood, and no longer range interactions can play any roles in this.  Since computation is quite efficient (sec. 6.3), I wonder if adding more layers helped anything or not.

Even though motivated from graph convolutions, when simplified as the paper suggests, the operations the model does are quite simple.  Compared to Duvenaud et al. 2015 and Li et al. 2016, the proposed method is simpler and does almost strictly less things.  So how would the proposed GCN compare against these methods?

Overall I think this model is simple, but the connection to graph convolutions is interesting, and the experiment results are quite good.  There are a few questions that still remain, but I feel this paper can be accepted.
review_title: Solid results.

final_score: rating: 7: Good paper, accept
review_content: The paper develops a simple and reasonable algorithm for graph node prediction/classification. The formulations are very intuitive and lead to a simple CNN based training and can easily leverage existing GPU speedups. 

Experiments are thorough and compare with many reasonable baselines on large and real benchmark datasets. Although, I am not quite aware of the literature on other methods and there may be similar alternatives as link and node prediction is an old problem. I still think the approach is quite simple and reasonably supported by good evaluations.  
review_title: Simple and reasonable approach

final_score: rating: null
review_content: This looks like a solid paper.  One question I had was: why is computation on GPU not much faster than CPU?  It seems like most of the operations required in training are large matrix multiplications and element-wise operations, which should be very suitable for GPUs.  In the paper this is not explained, maybe the sparsity of data played a role?
review_title: Why is GPU not much faster than CPU

Meta-review: The reviewers are in agreement that this paper is well written and constitutes a solid contribution to graph-based semi-supervised learning based on variants of CNNs.
Decision: Accept (Poster)