DyRep: Learning Representations over Dynamic Graphs
Representation Learning over graph structured data has received significant attention recently due to its ubiquitous applicability. However, most advancements have been made in static graph settings while efforts for jointly learning dynamic of the graph and dynamic on the graph are still in an infant stage. Two fundamental questions arise in learning over dynamic graphs: (i) How to elegantly model dynamical processes over graphs? (ii) How to leverage such a model to effectively encode evolving graph information into low-dimensional representations? We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes). Concretely, we propose a two-time scale deep temporal point process model that captures the interleaved dynamics of the observed processes. This model is further parameterized by a temporal-attentive representation network that encodes temporally evolving structural information into node representations which in turn drives the nonlinear evolution of the observed graph dynamics. Our unified framework is trained using an efficient unsupervised procedure and has capability to generalize over unseen nodes. We demonstrate that DyRep outperforms state-of-the-art baselines for dynamic link prediction and time prediction tasks and present extensive qualitative insights into our framework.
 The paper is very well written. The proposed approach is appropriate on modeling the node representations when the two types of events happen in the dynamic networks. Authors also clearly discussed the relevance and difference to related work. Experimental results show that the presented method outperforms the other baselines. Overall, it is a high-quality paper. There are only some minor comments for improving the paper: ν Page 6, there is a typo. “for node v by employing …” should be “for node u” ν Page 6, “Both GAT and GaAN has” should be “Both GAT and GaAN have” ν In section 5.1, it will be great if authors can explain more what are the “association events” and “communication events” with more details in these two evaluation datasets.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
DyRep: Learning Representations over Dynamic Graphs
Representation Learning over graph structured data has received significant attention recently due to its ubiquitous applicability. However, most advancements have been made in static graph settings while efforts for jointly learning dynamic of the graph and dynamic on the graph are still in an infant stage. Two fundamental questions arise in learning over dynamic graphs: (i) How to elegantly model dynamical processes over graphs? (ii) How to leverage such a model to effectively encode evolving graph information into low-dimensional representations? We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes). Concretely, we propose a two-time scale deep temporal point process model that captures the interleaved dynamics of the observed processes. This model is further parameterized by a temporal-attentive representation network that encodes temporally evolving structural information into node representations which in turn drives the nonlinear evolution of the observed graph dynamics. Our unified framework is trained using an efficient unsupervised procedure and has capability to generalize over unseen nodes. We demonstrate that DyRep outperforms state-of-the-art baselines for dynamic link prediction and time prediction tasks and present extensive qualitative insights into our framework.
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 is very well written. The proposed approach is appropriate on modeling the node representations when the two types of events happen in the dynamic networks. Authors also clearly discussed the relevance and difference to related work. Experimental results show that the presented method outperforms the other baselines. Overall, it is a high-quality paper. There are only some minor comments for improving the paper: ν Page 6, there is a typo. “for node v by employing …” should be “for node u” ν Page 6, “Both GAT and GaAN has” should be “Both GAT and GaAN have” ν In section 5.1, it will be great if authors can explain more what are the “association events” and “communication events” with more details in these two evaluation datasets.
assistant
 The paper is very well written. The proposed approach is appropriate on modeling the node representations when the two types of events happen in the dynamic networks. Authors also clearly discussed the relevance and difference to related work. Experimental results show that the presented method outperforms the other baselines. Overall, it is a high-quality paper. There are only some minor comments for improving the paper: ν Page 6, there is a typo. “for node v by employing …” should be “for node u” ν Page 6, “Both GAT and GaAN has” should be “Both GAT and GaAN have” ν In section 5.1, it will be great if authors can explain more what are the “association events” and “communication events” with more details in these two evaluation datasets.