Publication

The Costs of Overambitious Seeding of Social Products

International Conference on Complex Networks and their Applications


Abstract

Product-adoption scenarios are often theoretically modeled as “influence-maximization” (IM) problems, where people influence one another to adopt and the goal is to find a limited set of people to “seed” so as to maximize long-term adoption. In many IM models, if there is no budgetary limit on seeding, the optimal approach involves seeding everybody immediately. Here, we argue that this approach can lead to suboptimal outcomes for “social products” that allow people to communicate with one another. We simulate a simplified model of social-product usage where people begin using the product at low rates and then ramp their usage up or down depending upon whether they are satisfied with their experiences. We show that overambitious seeding can result in people adopting in suboptimal contexts, where their friends are not active often enough to produce satisfying experiences. We demonstrate that gradual seeding strategies can do substantially better in these regimes.

Related Publications

All Publications

ICML - July 24, 2021

Using Bifurcations for Diversity in Differentiable Games

Jonathan Lorraine, Jack Parker-Holder, Paul Vicol, Aldo Pacchiano, Luke Metz, Tal Kachman, Jakob Foerster

arXiv - July 8, 2021

First-Generation Inference Accelerator Deployment at Facebook

Michael Anderson, Benny Chen, Stephen Chen, Summer Deng, Jordan Fix, Michael Gschwind, Aravind Kalaiah, Changkyu Kim, Jaewon Lee, Jason Liang, Haixin Liu, Yinghai Lu, Jack Montgomery, Arun Moorthy, Satish Nadathur, Sam Naghshineh, Avinash Nayak, Jongsoo Park, Chris Petersen, Martin Schatz, Narayanan Sundaram, Bangsheng Tang, Peter Tang, Amy Yang, Jiecao Yu, Hector Yuen, Ying Zhang, Aravind Anbudurai, Vandana Balan, Harsha Bojja, Joe Boyd, Matthew Breitbach, Claudio Caldato, Anna Calvo, Garret Catron, Sneh Chandwani, Panos Christeas, Brad Cottel, Brian Coutinho, Arun Dalli, Abhishek Dhanotia, Oniel Duncan, Roman Dzhabarov, Simon Elmir, Chunli Fu, Wenyin Fu, Michael Fulthorp, Adi Gangidi, Nick Gibson, Sean Gordon, Beatriz Padilla Hernandez, Daniel Ho, Yu-Cheng Huang, Olof Johansson, Shishir Juluri, Shobhit Kanaujia, Manali Kesarkar, Jonathan Killinger, Ben Kim, Rohan Kulkarni, Meghan Lele, Huayu Li, Huamin Li, Yueming Li, Cynthia Liu, Jerry Liu, Bert Maher, Chandra Mallipedi, Seema Mangla, Kiran Kumar Matam, Jubin Mehta, Shobhit Mehta, Christopher Mitchell, Bharath Muthiah, Nitin Nagarkatte, Ashwin Narasimha, Bernard Nguyen, Thiara Ortiz, Soumya Padmanabha, Deng Pan, Ashwin Poojary, Ye (Charlotte) Qi, Olivier Raginel, Dwarak Rajagopal, Tristan Rice, Craig Ross, Nadav Rotem, Scott Russ, Kushal Shah, Baohua Shan, Hao Shen, Pavan Shetty, Krish Skandakumaran, Kutta Srinivasan, Roshan Sumbaly, Michael Tauberg, Mor Tzur, Hao Wang, Man Wang, Ben Wei, Alex Xiao, Chenyu Xu, Martin Yang, Kai Zhang, Ruoxi Zhang, Ming Zhao, Whitney Zhao, Rui Zhu, Lin Qiao, Misha Smelyanskiy, Bill Jia, Vijay Rao

UAI - July 23, 2021

High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces

David Eriksson, Martin Jankowiak

ICML - July 18, 2021

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, Stéphane Deny

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy