Kiran Tomlinson, Austin R. Benson
arXiv 23 May 2022 · Machine Learning · publishedNetwork Science (2023) · 2 citations (OpenAlex)
arXiv:2205.11365 · PDF · DOI · OpenAlex · Extracted main text
Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Traditional methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | K. Tomlinson and A. R. Benson (2021) Learning interpretable feature context effects in discrete choice | 1.000 | 6 | 4 | 100% |
| 2 | T. N. Kipf and M. Welling (2017) Semi-supervised classification with graph convolutional networks | 1.000 | 5 | 3 | 100% |
| 3 | J. Jia and A. R. Benson (2022) A unifying generative model for graph learning algorithms: Label propagation, graph convolutions, and combinations | 0.928 | 4 | 3 | 100% |
| 4 | D. McFadden (1973) Conditional logit analysis of qualitative choice behavior | 0.928 | 4 | 3 | 100% |
| 5 | Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip (2020) A comprehensive survey on graph neural networks | 0.928 | 4 | 3 | 100% |
| 6 | N. Aharony, W. Pan, C. Ip, I. Khayal, and A. Pentland (2011) Social fMRI: Investigating and shaping social mechanisms in the real world | 0.874 | 5 | 2 | 100% |
| 7 | A. Bower and L. Balzano (2020) Preference modeling with context-dependent salient features | 0.843 | 3 | 3 | 100% |
| 8 | A. Seshadri, A. Peysakhovich, and J. Ugander (2019) Discovering context effects from raw choice data | 0.843 | 3 | 3 | 100% |
| 9 | F. Feinberg, E. Bruch, M. Braun, B. H. Falk, N. Fefferman, E. M. Fei… (2020) Choices in networks: a research framework | 0.737 | 3 | 2 | 100% |
| 10 | J. Jia and A. R. Benson (2020) Residual correlation in graph neural network regression | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 74 scored citations.