Chainarong Amornbunchornvej, Ivan Brugere, Ariana Strandburg-Peshkin, Damien Farine, Margaret C. Crofoot, Tanya Y. Berger-Wolf
arXiv 4 Mar 2016 · cs.SI · publishedACM Transactions on Knowledge Discovery from Data (2018) · 19 citations (OpenAlex)
arXiv:1603.01570 · PDF · DOI · OpenAlex · Extracted main text
Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the "Coordination Initiator Inference Problem" and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time-series data from interacting entities.
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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 | Mattias Andersson, Joachim Gudmundsson, Patrick Laube, and Thomas Wo… (2008) Reporting leaders and followers among trajectories of moving point objects | 1.000 | 9 | 4 | 100% |
| 2 | David Kempe, Jon Kleinberg, and Éva Tardos (2003) Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD. ACM, 137–146 | 1.000 | 8 | 3 | 100% |
| 3 | Mikkel Baun Kjargaard, Henrik Blunck, Markus Wustenberg, Kaj Gronbas… (2013) Time-lag method for detecting following and leadership behavior of pedestrians from mobile sensing data. In Proceedings of the I… | 1.000 | 7 | 4 | 100% |
| 4 | Yan Liu, Taha Bahadori, and Hongfei Li (2012) Sparse-gev: Sparse latent space model for multivariate extreme value time serie modeling | 1.000 | 6 | 3 | 100% |
| 5 | Jia Li, Kaiser Asif, Hong Wang, Brian D Ziebart, and Tanya Y Berger-… (2016) Adversarial Sequence Tagging.. In IJCAI. 1690–1696 | 1.000 | 5 | 3 | 100% |
| 6 | John R.G Dyer, Anders Johansson, Dirk Helbing, Iain D Couzin, and Je… (2009) Leadership, consensus decision making and collective behaviour in humans | 0.811 | 4 | 2 | 100% |
| 7 | Song Wu and Quanbin Sun (2014) Computer Simulation of Leadership, Consensus Decision Making and Collective Behaviour in Humans | 0.737 | 3 | 2 | 100% |
| 8 | Sabine Stueckle and Dietmar Zinner (2008) To follow or not to follow: decision making and leadership during the morning departure in chacma baboons | 0.737 | 3 | 2 | 100% |
| 9 | Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd (1999) The PageRank Citation Ranking: Bringing Order to the Web | 0.644 | 2 | 2 | 100% |
| 10 | Ariana Strandburg-Peshkin, Damien R. Farine, Iain D. Couzin, and Mar… (2015) Shared decision-making drives collective movement in wild baboons self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 145 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis | 1.000 | 7 | 4 |
| 2 | Variable-lag Granger Causality for Time Series Analysis | 1.000 | 5 | 4 |