From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper47/Authors
comment: Thank you for your interest in our work and feedback on the proposed model.
We have now revised the version addressing your comments and more details
are provided in Appendix C.
title: Reply to comment from AnonReviewer2

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper47/Authors
comment: Thank you for your interest in our work and supporting comments.
We have added a new revision with more details on contributions
and related work in Appendix and also improved the presentation
as per your comments. 

From the spatial point of view, the current work considers an implicit
notion of space as rightly pointed out by you. Concretely, distance
between the nodes of graph in embedded space effects the pattern
of connections (topology) and communications (interaction) between
the nodes. Simultaneously, the dynamics "of" and "on" the network
affect these distances between nodes in embedded space. If one wants
to introduce explicit spatial features (for e.g. location in 
transportation network or mobile communication network), 
one simple appraoch would be to add such features as part of Eq. 1. 
Another approach could be to combine the temporal attentive operator 
with a local spatial operator (as studied in area of geometric deep learning) 
to build a sophisticated spatio-temporal operator. Finally, our Social
Evolution Dataset is indeed a spatio-temporal dataset as it contains location 
information of users. Although we do not use location explicity, self-propagation
and exogenous components will capture a weaker form of such features and further
location can be added as a feature in Eq 1. as mentioned above.
title: Reply to comment from AnonReviewer1

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper47/AnonReviewer1
relevance: 3: borderline
impact: 5: very high
novelty: 4: high

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper47/AnonReviewer1
evaluation: The overall material presented by the paper are very impressive, with a high performance against existing methods in graph representation and link/time prediction. The issues addressed in the paper are relevant to current research, and provide a framework for learning temporal attentive process between nodes. The paper differentiates a communication event as a local effect and an association even as a global effect, which is an insightful way to address the dynamic nature of nodes events. The high performance of such representation is also evidential and brings the research community a powerful framework for encoding high-dimensional dynamic graph information.

On the other hand, the paper tries to incorporate a lot of information into the limited pages, which made the flow of the text hard to follow. There are also some visual disturbance near mathematical symbols (overlapping super/subscript/bar) and Figure 1-2 being cramped (and captions being covered by figures). Although it is understandable that there is a lot of information to include in such limited space, these issues degrade the overall presentation quality of the paper, despite good contents. Another minor issue, is the paper does not directly address the spatial temporal relationship between the data (although one can argue spatial information are embedded in the graph structure), which made the paper seem less relevant to a spatiotemporal workshop.

Overall the contents are impressive and the proposing framework could definitely bring new insight to the research community.

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper47/AnonReviewer2
evaluation: This paper introduces DyRep, a framework to better handle the modeling of both global and local dynamics in a graph. They introduce a temporal point process based attentional mechanism that allows for updating the graph's structure as well as how the nodes themselves are changing over time. The model achieves favorable performance on two datasets. This was an interesting application of attention to learning dynamic latent representations. One thing that wasn't very clear to me though was where this work fits in into the current literature. Specifically, it would have been nice for the work to more explicitly state their contribution, and where the novelty in their method lies in relation to the related work.

From: NIPS.cc/2018/Workshop/Spatiotemporal/Paper47/AnonReviewer2
relevance: 4: high
impact: 3: borderline
novelty: 3: borderline

From: ~Doyup_Lee1
comment: I am interested in graph representation and this paper cover important issue.
However, this workshop allows maximum 4 pages, excluding references.

title: Why this paper has 5 pages?