Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors consider the problem of task migration for decision making tasks in social networks for the purpose of managing contagion events. The authors begin by formally defining a social network (and associated social contagions) as a directed graph of users, where a set of seed users initiate a social contagion, with contagions spreading through edges at a rate defined by a distribution on each edge.

The authors then define four different contagion management problems, the last of which is the task migration problem. In this setting, we have two contagion management tasks (source and target) on the same social network and have samples from the source task. The goal is to minimize a loss with respect to the target task using those source task samples.

The authors propose an algorithm for this task migration problem which they dub social-inverse. They bound the generalization error of social inverse and compare their algorithm in simulated experiments to supervised learning algorithms and a heuristic that selects high-degree users as seed nodes.
strengths_and_weaknesses: This paper addresses an important problem (task migration) in a very general setting that can be applied to many different instances (contagion management in social networks). Unfortunately I am unable to leave an educated review since this paper seems to be targeted at a highly technical research sub-community which I have had no prior exposure to.

My primary concern is that this paper might not be accessible to most of Neurips' audience and thus might be better received at more theoretical conferences like STOC or COLT. However, I do not feel as though this should be a disqualifying factor for acceptance, especially if there is a large enough community at Neurips that would find this paper useful.

Going through the theoretical results, the assumptions seem reasonable and I can't see any glaring red flags in their resulting theorems.

I do not feel like I have enough context to critically interpret the significance of the improvements in the experimental results. 

Overall this paper seems sound to me, but I have concerns about the accessibility of the paper. Hence I recommend a borderline accept with very low confidence. I think the authors could significantly improve the paper if they spent some time providing more intuition in the first few sections and also added some concrete examples to aid the interpretation of their definitions.
questions: How closely do the assumptions of this work mirror real-world practices in social contagion management?
For example, the introduction states that "For example, a network manager may need to work on a rumor blocking task, but they only have historical data collected in solving viral marketing tasks." are there any existing cases where such a network manager would employ any methods that are remotely similar to social-inverse instead of just proceeding based on intuition/industry best practices for their interventions?
limitations: Limitations were well addressed by the author.
review_title: null

final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: The paper considers a semi-opaque-box influence network and the cumulative impacts from information dissemination at a few seed nodes. The network is semi-opaque in the sense that we know the general connectivity of the nodes, but not the actual realization of the influence, which is sampled from an unknown distribution with support by the observed connections. To make up for the observability gap, the paper suggests to use previous examples of information diffusion, collected in the form of which targets to reach (queries) and which seeds would maximize the coverage of the targets after diffusion (decisions). It then proposes to use random samples of possible realizations to solve a max-margin (SVM) optimization problem to find out the most likely linear combination of the realizations. The paper finally uses the learned weighted combination of realizations to decide for optimal solutions in new information diffusion tasks. Theoretical and empirical studies were included.
strengths_and_weaknesses: Strengths:
* Quality: The paper is nicely presented with thorough empirical studies.
* Originality: The paper solves an essentially "graph-kernel" problem using inspirations from random kitchen sink. This sounds like a reasonable idea.

Weaknesses:
* Theory: The presentation is not entirely clear and I have some questions about some unexplained terms in the key algorithm and theoretical analysis.
* Significance: We have to accept the assumption that the training data is presented as query-decision pairs, instead of the more commonly used decision-impact pairs, that is, the actual coverage caused by the seed nodes.
* Ethics: The paper is missing ethics discussions on a potentially sensitive topic.
questions: * Line 172. Why do you introduce a Gaussian distribution after you learned the optimal combinations of realizations? Doesn't this introduce pure noise?
* Line 185. Related, the generalization error considers the mean of the sample you use for inference, yet in the algorithm description, you use only one point in the sample for inference. Does this generalization error cover the full risk?
* Line 191. I do not understand the use of beta. Also, why do you analyze a binary loss when your objective contains an optimization sub-problem?
* Table 1. Can you elaborate on Naive Bayes? It seems to be the second-best in the considered baselines, just below High Degree.
limitations: Overall, I can get some inspirations from the proposed method and empirical studies, but some key aspects in the algorithm were left unexplained, namely eta and the extra step of Gaussian sampling. The theory presentation does not meet the expectation of the NeurIPS community, perhaps because too many new concepts were introduced without good explanations.

Though the paper is purely methodological, I flagged it for ethics reviews due to some word choices, such as information containment, which may lead to a limitation of people's access to opportunities. The authors should be also be advised on the creation of an ethics discussion section.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper studies an inverse decision-making problem for task migrations on contagion management. Specifically, it investigates how prior decision-making on diffusion containment can be migrated to diffusion enhancement; and vice versa. It provides a theoretical analysis of how different diffusion management tasks can be correlated such that samples from one task can be useful for another task. It performs empirical analysis on four graphs, comparing with several benchmarks (used for supervised learning) to evaluate the performance. 
strengths_and_weaknesses: Strengths: 
I find the idea of task migration on contagion management novel and important (although I have some concerns about the detailed setup (see weakness 1). The paper provides rigorous theoretical analysis and shows empirical effectiveness on four datasets. 

Weaknesses:
(1) I think the setup that the platform only observes one type of task (either diffusion containment or enhancement) does not seem realistic. It is more likely that the platform will observe a combination of both. I think this is a crucial question: How realistic is the task migration problem proposed in this paper? 

(2)  Given that many models have been proposed in the diffusion literature (also cited by the paper), designing benchmarks w.r.t. based on these models is important to show the effectiveness of the methods, rather than other ML methods that do not know the underlying diffusion process.  
questions: 
(1) In remark 1, the paper mentioned that the model generalizes popular diffusion models, including the linear threshold model, which requires a percentage or number of neighbors to adopt before the users adopt. However, in lines 79—80 on P2, the paper mentions that the node will only be activated by "first in-attempting neighbors" or activated by the cascade with the smallest index. These two scenarios both conflict with the linear threshold model. Why can't a node be activated by two or more cascades? In reality, a user only observes his/her neighbor's decisions, rather than which cascade resulted in the neighbors' decision. 

(2) L88–89, why is the subgraph weighted? From the description in Line 71—72, I think the subgraph is attributed instead of weighted? 

(3) There are two decision-making problems in the paper, one is the decision-making of users, and the other is the decision-making of the central planner (I believe referred to as agent by the paper). Given that both are important concepts yet are entirely different, I suggest distinguishing the two concepts to avoid confusing the readers, e.g., user decision-marking vs. planner decision-making). 

(4) \beta is an important parameter that controls the trade-off between estimation and approximation errors. How can it be tuned? It seems that the experiment section directly uses beta = 1. If it cannot be systematically tuned, it might also help to show the robustness of the method w.r.t. different beta 

(5) Regarding the experiment, I think it will help add a benchmark, which assumes either a linear threshold model or an independent cascade. The parameters in these models can be learned based on different realizations. That is, since there is rich research on the diffusion model in sociology, it seems sensible to use them directly in such a migration task. 

(6) I wonder how realistic in practice do we assume the platform has information about all DC but no DE, or all DE but no DC? It seems more reasonable to observe a mix of both. Can you provide some insights on how the method performs (especially compared with the benchmarks) if this is the case? 

(7) Any insights on why the benchmarks perform this badly on ER? 

(8) Minor: There is a typo on line 84, "For sample" —> "For example"

limitations: NA
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: The paper presents a framework for task migration in the context of contagion management in social networks, in which, a target decision-making task is solved using the historical data collected from another task. The authors define a generic problem formulation that covers the tasks of diffusion enhancement as well as diffusion containment, and further derive a reformulated optimization problem that can be coupled with several classic algorithms to provide an approximated solution in a polynomial time.
strengths_and_weaknesses: Strengths:

+ Originality: The paper develops a novel framework for task migration between two classes of social management tasks with a generic problem definition and (re-)formulation of the objectives.

+ Quality: The authors theoretically analyze the generalization ability of their approach across tasks and show that the generalization error is bounded. Subsequently, they identify potential conditions on the design choices that can affect the generalizability, and thus, some aspects/parameters are chosen accordingly. In addition, they conduct considerable empirical study evaluating the performance of their method in various setups and compared to several baselines.

Weaknesses:

- Significance: The proposed approach is not well located within the literature, and the discussion of the related work seems to be limited. Additionally, in terms of approaches, it is mentioned that the ideas are closely related to structure prediction methods (refs. 40-42), but the  connection to those work is not clarified; what are the similarities/differences with those methods? in what sense the framework is novel compared to them? Moreover, the generalization of the approach to other domains and problems and the applicability of the work on real network data is not clear. As a result, the outcomes only impact a narrow scope which leads to limited significance.

- Clarity: The paper is well written, but it is a bit hard to follow. The writeup could be improved in terms of structure and choice of content, Some details about the background information and algorithmic characteristic can be expanded.


questions: - How the concepts of "task migration" relate to "transfer learning"?

- It is not completely clear what the authors mean by learning-based methods. Isn't your approach a learning framework?


limitations: No discussion is provided in that regard. However, given the nature of the work, which is related to the social network analysis, a discussion about societal impact of the approach would be interesting.
review_title: null

Meta-review: metareview: Strengths:
* novel formulation for task migration in social management tasks 
* theoretical analysis: generalization bound
* results shed light on certain possible design choices
* adequate empirical evaluation on simulated data

Weaknesses:
* formalization may be too restrictive to capture realistic settings (e.g., observing only one type of task)
* connections to some related literature not clearly established
* some concerns regarding baselines used in experiments, or lack thereof
* societal implication not discussed or properly acknowledged in authors’ response **(see ethics section below)**


Summary:  
All reviewers agree that the proposed problem of task migration for network diffusion is interesting, and that the proposed formal framework is elegant; some reviewers, however, found the framework to be somewhat restrictive in its ability to relate to real-world diffusive processes. Theoretical results seem sound, but to fully appreciate their novelty, it would help if the authors provide more details and make concrete connections to the literature on which they draw. The authors’ response to reviewers questions were helpful in this regard. Experimental results look encouraging; nonetheless, several reviewers raised concerns regarding the adequacy of certain baselines, or the lack of comparison to methods that include explicit diffusion modeling. Authors’ responses in this regard were only partially satisfactory.


**Ethics:**  
Reviews by two designated ethics reviewers strongly suggest that the paper frames the decision task it studies in a very one-sided manner, presenting mostly possible benefits (e.g., minimizing the spread of misinformation) and lacking to acknowledge for its evident risks (e.g., maximizing the spread of misinformation). The dual objectives studied—diffusion enhancement (Problem 1) and containment (Problem 2)—make it very clear that virtually every concrete task in this space has potential dual use. **Unfortunately, the authors do not discuss this in the paper, nor were their responses in the discussion appeasing.** Due to this, acceptance is made conditional on the authors’ ability to clearly convey in their writing, and *in the early parts of the paper*, the risks that naturally follow from their proposed work, as they relate to the societally-significant applications they discuss.


confidence: Less certain
Decision: Accept