Dynamic Network Analysis
Finding patterns of social interaction within a population has applications from epidemiology and marketing to conservation biology and behavioral ecology. An intrinsic characteristics of societies is their continual change. Yet, few analysis methods are explicitly dynamic. We are working on novel conceptual and computational frameworks to accurately describe the social context of an individual at time scales matching changes in individual and group activity. Current projects in this direction include finding communities and critical individuals in dynamic networks, and fine-grained interaction prediction in dynamic networks. [wiki]
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Sibling Relationship Reconstruction
Please see our Wiki Page for more information.
Falcons and other birds of prey are extremely secretive about their lives. Sharks are hard to catch and study because they live in water. Cowbirds leave eggs in other birds' nests and let them raise the cowbird chicks. One of the things common to all these species is that it is difficult to study their mating system. It is even difficult to identify which animals or plants are siblings. Yet, this simple fact is necessary for conservation, animal management, and understanding of evolutionary mechanisms.
Our team has developed the first efficient computational method for reconstruction of sibling relationships from genetic data geared especially to wild populations. Now, given a genetic sample of the individuals from the same generation, we can identify full siblings without any information on the parents. Unlike other methods, ours does not assume anything about the data beyond the simple Mendelian laws of inheritance and does not require any a priori knowledge about the population. Our method reconstructs known sibling groups exactly on data from such diverse species as cod, shrimp, flies, and radishes. Biologists now can use our method to find out more about mating systems by just taking a genetic sample from chicks in nests or young shark in shallow waters.
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