# Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations

arXiv: https://arxiv.org/abs/2209.10493 · HTML: https://mlsanity.com/paper/2209.10493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 101 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 22 of 22)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [Deep Set Prediction Networks](https://mlsanity.com/paper/1906.06565/) (2019)
- [Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency](https://mlsanity.com/paper/1404.0900/) (2014)
- [Fake News Mitigation via Point Process Based Intervention](https://mlsanity.com/paper/1703.07823/) (2017)
- [The Anatomy of a Scientific Rumor](https://mlsanity.com/paper/1301.2952/) (2013)
- [Learning Linear Programs from Optimal Decisions](https://mlsanity.com/paper/2006.08923/) (2020)
- [Generalized Inverse Optimization through Online Learning](https://mlsanity.com/paper/1810.01920/) (2018)
- [DISCO: Influence Maximization Meets Network Embedding and Deep Learning](https://mlsanity.com/paper/1906.07378/) (2019)
- [The Simple Rules of Social Contagion](https://mlsanity.com/paper/1308.5015/) (2013)
- [Scalable Influence Estimation in Continuous-Time Diffusion Networks](https://mlsanity.com/paper/1311.3669/) (2013)
- [Learning Influence Functions from Incomplete Observations](https://mlsanity.com/paper/1611.02305/) (2016)
- [Quantile Inverse Optimization: Improving Stability in Inverse Linear Programming](https://mlsanity.com/paper/1908.02376/) (2019)
- [An Efficient Randomized Algorithm for Rumor Blocking in Online Social Networks](https://mlsanity.com/paper/1701.02368/) (2017)
- [StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks](https://mlsanity.com/paper/2009.14337/) (2020)
- [Diffusion Source Identification on Networks with Statistical Confidence](https://mlsanity.com/paper/2106.04800/) (2021)
- [USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems](https://mlsanity.com/paper/2107.07508/) (2021)
- [Predicting Adoption Probabilities in Social Networks](https://mlsanity.com/paper/1309.6369/) (2013)
- [Wasserstein Distributionally Robust Inverse Multiobjective Optimization](https://mlsanity.com/paper/2009.14552/) (2020)
- [Rooting out the Rumor Culprit from Suspects](https://mlsanity.com/paper/1301.6312/) (2013)
- [On Multi-Cascade Influence Maximization: Model, Hardness and Algorithmic Framework](https://mlsanity.com/paper/1912.00272/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)

## Abstract (arXiv)

Considering two decision-making tasks A and B, each of which wishes to compute an effective textitdecision Y for a given textitquery X, can we solve task B by using query-decision pairs (X, Y) of A without knowing the latent decision-making model? Such problems, called textitinverse decision-making with task migrations, are of interest in that the complex and stochastic nature of real-world applications often prevents the agent from completely knowing the underlying system. In this paper, we introduce such a new problem with formal formulations and present a generic framework for addressing decision-making tasks in social contagion management. On the theory side, we present a generalization analysis for justifying the learning performance of our framework. In empirical studies, we perform a sanity check and compare the presented method with other possible learning-based and graph-based methods. We have acquired promising experimental results, confirming for the first time that it is possible to solve one decision-making task by using the solutions associated with another one.