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Reconstruction of sibling relationships from genetic data is an important component of many biological applications. In particular, the growing application of molecular markers (microsatellites) to study wild populations of plant and animals has created the need for new computational methods of establishing pedigree relationships, such as sibgroups, among(More)
From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on how best to sample from networks. We investigate connections between specific biases and various measures of structural representativeness. We show that certain biases are, in(More)
We consider the problem of multiple description scalar quantizers and describing the achievable rate-distortion tuples in that setting. We formulate it as a combinatorial optimization problem of arranging numbers in a matrix to minimize the maximum difference between the largest and the smallest number in any row or column. We develop a technique for(More)
In this paper we propose three methods of measuring betweenness of individuals in networks which are best modeled as graphs with explicit time ordering on their edges. The betweenness centrality index is one of the basic measure in the analysis of social networks, but most of the work done for measuring the betweenness index of individuals is based on the(More)
I do not believe that the accident of birth makes people sisters and brothers. It makes them sib-lings. Gives them mutuality of parentage. Abstract New technologies for collecting genotypic data from natural populations open the possibilities of investigating many fundamental biological phenomena, including behavior , mating systems, heritabilities of(More)
We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the(More)
We propose a novel method, based on concepts from expander graphs, to sample communities in networks. We show that our sampling method, unlike previous techniques, produces subgraphs representative of community structure in the original network. These generated subgraphs may be viewed as stratified samples in that they consist of members from most or all(More)
Social interactions are conduits for various processes spreading through a population, from rumors and opinions to behaviors and diseases. In the context of the spread of a disease or undesirable behavior, it is important to identify blockers: individuals that are most effective in stopping or slowing down the spread of a process through the population.(More)
There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective is, given a series of observations, to predict exactly when certain interactions will occur in the future. We propose a representation for this type of temporal data and a generic,(More)