Publications
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Exploiting the relationship between structural modularity and sparsity for faster network evolution
Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, , 1173-1176, 2015
Status: Published
Citations:
Cite: [bibtex]
Abstract: A network is structurally modular if it can be divided into tightly connected groups which are weakly connected or disconnected from each other. Such networks are known to be capable of fast evolutionary adaptation, which makes modularity a highly desired property of networks involved in evolutionary computation. Modularity is known to correlate positively with another network parameter, sparsity. Based on this relationship, we hypothesize that starting evolution with a population of sparse networks should increase the modularity of the evolved networks. We find that this technique can enhance the performance of an existing technique for modularity evolution, multiobjective performance-connection cost (P&CC) optimization, and enable a multiobjective algorithm which maximizes performance and minimizes time since last mutation to produce modular solutions almost as efficiently as the P&CC optimization does.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
- Josh Bongard, Victor Zykov, Hod Lipson. Resilient Machines Through
Continuous Self-Modeling. Science 314, 1118 (2006). [Journal Page] - Joey Anetsberger and Josh Bongard. Robots can ground crowd-proposed symbols by forming theories of group mind. Proceedings of the Artificial Life Conference 2016. [Link to Proceedings]
- Sam Kriegman, Nick Cheney, and Josh Bongard. How morphological development can guide evolution. arXiv 2017. [arXiv]
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
- Peter Sheridan Dodds , Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. PLoS ONE 2011. [Journal Page].
- Lewis Mitchell , Morgan R. Frank, Kameron Decker Harris, Peter Sheridan Dodds, Christopher M. Danforth. The Geography of Happiness: Connecting Twitter Sentiment and Expression, Demographics, and Objective Characteristics of Place. PLoS ONE 2013. [Journal Page].
- Andrew G Reece and Christopher M Danforth. Instagram photos reveal predictive markers of depression. EPJ Data Science 2017. [Journal Page].
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
- Laurent Hébert‐Dufresne Adam F. A. Pellegrini Uttam Bhat Sidney Redner Stephen W. Pacala Andrew M. Berdahl. Edge fires drive the shape and stability of tropical forests. Ecology Letters 2018. [Journal Page]
- Samuel V. Scarpino, Antoine Allard, Laurent Hébert-Dufresne. The effect of a prudent adaptive behaviour on disease transmission. Nature Physics 2016. [Journal Page]
- Laurent Hébert-Dufresne, Joshua A. Grochow, Antoine Allard. Multi-scale structure and topological anomaly detection via a new network statistic: The onion decomposition. Nature Scientific Reports 2016. [Journal Page]
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
- Paul D. H. Hines, Ian Dobson, Pooya Rezaei. Cascading Power Outages Propagate Locally in an Influence Graph That is Not the Actual Grid Topology. IEEE Transactions on Power Systems ( Volume: 32, Issue: 2, March 2017 ). [Journal Page]
- Mert Korkali, Jason G. Veneman, Brian F. Tivnan, James P. Bagrow & Paul D. H. Hines. Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence. Scientific Reports volume 7, Article number: 44499 (2017. [Journal Page]
- Pooya Rezaei, Paul D. H. Hines, Margaret J. Eppstein. Estimating Cascading Failure Risk With Random Chemistry. IEEE Transactions on Power Systems ( Volume: 30, Issue: 5, Sept. 2015 ). [Journal Page]
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).
- Y.-Y. Ahn, J. P. Bagrow and S. Lehmann. Link communities reveal multiscale complexity in networks. Nature, 466: 761-764 (2010). [Journal Page].
- M. R. Frank, J. R. Williams, L. Mitchell, J. P. Bagrow, P. S. Dodds, C. M. Danforth. Constructing a taxonomy of fine-grained human movement and activity motifs through social media. In preparation. (2015). [Journal Page].
- J. P. Bagrow and L. Mitchell. The quoter model: a paradigmatic model of the social flow of written information. To appear, Chaos (2018). [Journal Page].
