Modeling dynamically-evolving, multi-relational graph data has received a surge of interests with the rapid growth of heterogeneous event data. However, predicting future events on such data requires global structure inference over time and the ability to integrate temporal and structural information, which are not yet well understood. We present Recurrent Event Network (RE-NET), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e.g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events. RE-NET employs a recurrent event encoder to model the temporally conditioned joint probability distribution for the event sequences, and equips the event encoder with a neighborhood aggregator for modeling the concurrent events within a time window associated with each entity. We apply teacher forcing for model training over historical data, and infer graph sequences over future time stamps by sampling from the learned joint distribution in a sequential manner. We evaluate the proposed method via temporal link prediction on five public datasets. Extensive experiments 1 demonstrate the strength of RE-NET, especially on multi-step inference over future time stamps.
The paper proposes a recurrent and autorgressive architecture to model temporal knowledge graphs and perform multi-time-step inference in the form of future link prediction. Specifically, given a historical sequence of graphs at discrete time points, the authors build sequential probabilistic approach to infer the next graph using joint over all previous graphs factorized into conditional distributions of subject, relation and the objects. The model is parameterized by a recurrent architecture that employs a multi-step aggregation to capture information within the graph at particular time step. The authors also propose a sequential approach to perform multi-step inference. The proposed method is evaluated on the task of future link prediction across several baselines, both static and dynamic, and ablation analysis is provided to measure the effect of each component in the architecture.
The authors propose to model temporal knowledge graphs with the key contribution being the sequential inference and augmentation of RNN with multi-step aggregation. The paper is well written in most parts and provides adequate details with some exceptions. I appreciate the extended ablation analysis as it helps to segregate the effect of each component very clearly. However, there are several major concerns which makes this paper weaker:
- The paper approaches temporal knowledge graphs in discrete-time fashion where multiple events/edges are available at each time step. While this is intuitive, the authors fail to position the paper in light of various existing discrete-time approaches that focus on representation learning over evolving graphs [1,2,3,4,5]. Related work mentions [1] learns evolving representations but all these methods can do future link prediction and hence this is a big miss for the paper. A discussion and comparison with these approaches is certainly required as most of static and dynamic baselines currently compared also focus on learning representations, hence that is not a valid argument to miss comparison. 
- The baselines tested by the authors are either support static graphs, supports interpolation or supports continuous time data. However, as the authors explicitly propose a discrete time model starting from Section 3, it is important to perform experiments on atleast few of the discrete time baselines to demonstrate the efficacy of the proposed method. For instance, authors can augment relation as extra feature or use their encoders and optimization function to perform experiments e.g. Evolve-GCN  only require to replace GCN with R-GCN.
- From the ablation it is clear that aggregation is the most important component as without it, the performance drops much closer to ConvE which is a static baseline and significantly worse than other RE-Net variants. However, the aggregation techniques are not novel contributions but augmentation to the RNN architecture. Hence it is important to show how augmenting aggregation module with other baselines (for instance, ConvE and TA-DistMult)) and the above mentioned discrete baselines would affect the performance of these baselines. 
- While the authors describe attentive Pooling Aggregator, the experiments only show mean aggregator and multi-step one. Is there a reason Attentive pooling is not used for any experiments? 
-It appears that global vector H_t is not playing significant role based on ablation study. Can the authors explain why that si the case? Also, what aggregation is used to compute H_t? Is it sum over all previous h_t's?
- Algorithm 1 is not very clearly explained. When the authors mention that they only use one sample, does that mean a single subject is sampled at each time point t'? If so, how do you ensure the coverage is good across subjects in the newly generated graph? I admit I am not clear on this and would recommend the authors to elaborate in response and also in the paper. Also, the inference computation complexity is concerning. While it seems fine for the provided dataset, most real-world graphs have billion of nodes and I all of E, L and D would be larger for such graphs. This seems to put a strict limitation on scalability of inference module. 
- It is not clear what is the difference between RE-NET and RE-NET w. GT. Could the authors elaborate this more? It seems the authors do not update history when they perform RE-NET w/o multi-step. However, in the RE-NET w. GT, where is the ground truth history used in Algorithm 1?
- The time span expansion for WIKI and YAGO is very unnatural and it is not clear if these experiments provide any value. For instance, can the authors show that in multi-step inference scheme, they can actually predict events at multiple time points corresponding to time span events in actual dataset? As multiple triplets can appear at consecutive time points, the current modification just makes them equivalent which doesn't seem correct. 
I am willing to revisit my score if the above concerns are appropriately addressed and requested experiments are provided.
[1] Evolve-GCN: Evolving Graph Convolutional Networks for Dynamic Graphs, Pareja et. al.
[2] DynGEM: Deep embedding method for dynamic graphs, Goyal et. al.
[3] dyngraph2vec: Capturing network dynamics using dynamic graph representation learning, Goyal et. al.
[4] Dynamic Network Embedding by Modeling Triadic Closure Process, Zhou et. al.
[5] Node Embedding over Temporal Graphs, Singer et. al.