Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs
Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. However, in many of the applications, the underlying graph changes over time and existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs based on a tensor algebra framework. Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique. Theoretical results that establish the connection between the proposed tensor approach and spectral convolution of tensors are developed. Numerical experiments on real datasets demonstrate the usefulness of the proposed method for an edge classification task on dynamic graphs.
The paper proposed a new type of graph embedding technique for dynamic graphs based on tensor representation (node x feature x time). Experiments on edge classification demonstrate improved prediction accuracy. + Clear writing with tensor notations and explanation is well-structured + Improved prediction results on 3/4 real-world dynamic graph datasets - The theoretical results are a bit artificial. The tensor eigendecomposition used in this paper and [Kilmer and Martin] is for slices of the tensor, similarly for FFT and convolution. The technique is a trivial generalization from matrix results. - The paper is missing a large body of baselines, both from the network science community (non-deep learning methods) and from this community (diffusion convolutional RNNs, graph attention networks, etc). - The method doesn*t scale well, especially for graphs with long-term dynamics. It would be good to show the scaling behavior of the proposed model.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs
Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. However, in many of the applications, the underlying graph changes over time and existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs based on a tensor algebra framework. Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique. Theoretical results that establish the connection between the proposed tensor approach and spectral convolution of tensors are developed. Numerical experiments on real datasets demonstrate the usefulness of the proposed method for an edge classification task on dynamic graphs.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper proposed a new type of graph embedding technique for dynamic graphs based on tensor representation (node x feature x time). Experiments on edge classification demonstrate improved prediction accuracy. + Clear writing with tensor notations and explanation is well-structured + Improved prediction results on 3/4 real-world dynamic graph datasets - The theoretical results are a bit artificial. The tensor eigendecomposition used in this paper and [Kilmer and Martin] is for slices of the tensor, similarly for FFT and convolution. The technique is a trivial generalization from matrix results. - The paper is missing a large body of baselines, both from the network science community (non-deep learning methods) and from this community (diffusion convolutional RNNs, graph attention networks, etc). - The method doesn*t scale well, especially for graphs with long-term dynamics. It would be good to show the scaling behavior of the proposed model.
assistant
The paper proposed a new type of graph embedding technique for dynamic graphs based on tensor representation (node x feature x time). Experiments on edge classification demonstrate improved prediction accuracy. + Clear writing with tensor notations and explanation is well-structured + Improved prediction results on 3/4 real-world dynamic graph datasets - The theoretical results are a bit artificial. The tensor eigendecomposition used in this paper and [Kilmer and Martin] is for slices of the tensor, similarly for FFT and convolution. The technique is a trivial generalization from matrix results. - The paper is missing a large body of baselines, both from the network science community (non-deep learning methods) and from this community (diffusion convolutional RNNs, graph attention networks, etc). - The method doesn*t scale well, especially for graphs with long-term dynamics. It would be good to show the scaling behavior of the proposed model.