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Coordination Event Detection and Initiator Identification in Time Series Data

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

Abstract

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.

Citation extraction

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in-text mentions
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distinct cited
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Mattias Andersson, Joachim Gudmundsson, Patrick Laube, and Thomas Wo… (2008) Reporting leaders and followers among trajectories of moving point objects1.00094100%
2David Kempe, Jon Kleinberg, and Éva Tardos (2003) Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD. ACM, 137–1461.00083100%
3Mikkel 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…1.00074100%
4Yan Liu, Taha Bahadori, and Hongfei Li (2012) Sparse-gev: Sparse latent space model for multivariate extreme value time serie modeling1.00063100%
5Jia Li, Kaiser Asif, Hong Wang, Brian D Ziebart, and Tanya Y Berger-… (2016) Adversarial Sequence Tagging.. In IJCAI. 1690–16961.00053100%
6John R.G Dyer, Anders Johansson, Dirk Helbing, Iain D Couzin, and Je… (2009) Leadership, consensus decision making and collective behaviour in humans0.81142100%
7Song Wu and Quanbin Sun (2014) Computer Simulation of Leadership, Consensus Decision Making and Collective Behaviour in Humans0.73732100%
8Sabine Stueckle and Dietmar Zinner (2008) To follow or not to follow: decision making and leadership during the morning departure in chacma baboons0.73732100%
9Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd (1999) The PageRank Citation Ranking: Bringing Order to the Web0.64422100%
10Ariana Strandburg-Peshkin, Damien R. Farine, Iain D. Couzin, and Mar… (2015) Shared decision-making drives collective movement in wild baboons self0.64422100%

Showing the top 10 of 145 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis1.00074
2Variable-lag Granger Causality for Time Series Analysis1.00054