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The Strength of Weak Ties: A Network Theory Revisited
 Sociological Theory
, 1982
"... In this chapter I review empirical studies directly testing the ..."
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Cited by 284 (2 self)
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In this chapter I review empirical studies directly testing the
NeighborhoodBased Models for Social Networks
 Sociological Methodology
, 2002
"... Harrison White and several anonymous reviewers for valuable comments on the work. We argue that social networks can be modeled as the outcome of processes that occur in overlapping local regions of the network, termed local social neighborhoods. Each neighborhood is conceived as a possible site of i ..."
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Cited by 54 (4 self)
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Harrison White and several anonymous reviewers for valuable comments on the work. We argue that social networks can be modeled as the outcome of processes that occur in overlapping local regions of the network, termed local social neighborhoods. Each neighborhood is conceived as a possible site of interaction and corresponds to a subset of possible network ties. In this paper, we discuss hypotheses about the form of these neighborhoods, and we present two new and theoretically plausible ways in which neighborhoodbased models for networks can be constructed. In the first, we introduce the notion of a setting structure, a directly hypothesized (or observed) set of exogenous constraints on possible neighborhood forms. In the second, we propose higherorder neighborhoods that are generated, in part, by the outcome of interactive network processes themselves. Applications of both approaches to model construction are presented, and the developments are considered within a general conceptual framework of locale for social networks. We show how assumptions about neighborhoods can be cast within a hierarchy of increasingly complex models; these models represent a progressively greater capacity for network processes to “reach ” across a network through long cycles or semipaths. We argue that this class of models holds new promise for the development of empirically plausible models for networks and networkbased processes. 2 1.
Small and other worlds: Global network structures from local processes
 American Journal of Sociology
, 2005
"... Using simulation, we contrast global network structures—in particular, small world properties—with the local patterning that generates the network. We show how to simulate Markov graph distributions based on assumptions about simple local social processes. We examine the resulting global structures ..."
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Cited by 18 (1 self)
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Using simulation, we contrast global network structures—in particular, small world properties—with the local patterning that generates the network. We show how to simulate Markov graph distributions based on assumptions about simple local social processes. We examine the resulting global structures against appropriate Bernoulli graph distributions and provide examples of stochastic global “worlds, ” including small worlds, long path worlds, and nonclustered worlds with many fourcycles. In light of these results we suggest a locally specified social process that produces small world properties. In examining movement from structure to randomness, parameter scaling produces a phase transition at a “temperature ” where regular structures “melt ” into stochastically based counterparts. We provide examples of “frozen ” structures, including “caveman ” graphs, bipartite structures, and cyclic structures.
Bias reduction in traceroute sampling: Towards a more accurate map of the internet
 In Proceedings of the 5th Workshop on Algorithms and Models for the WebGraph (WAW2007
"... Internet ..."
Estimation of Diffusion Processes From Incomplete Data A Simulation Study
"... Eventhistory analysis of the diffusion of practices in a social system can show how actors are influenced by each other as well as by their own characteristics. The presumption that complete data on the entire population are essential to draw valid inferences about diffusion processes has been a ma ..."
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Cited by 1 (0 self)
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Eventhistory analysis of the diffusion of practices in a social system can show how actors are influenced by each other as well as by their own characteristics. The presumption that complete data on the entire population are essential to draw valid inferences about diffusion processes has been a major limitation in empirical analyses and has precluded diffusion studies in large populations. The authors examine the impacts of several forms of incomplete data on estimation of the heterogeneous diffusion model proposed by Strang and Tuma. Left censoring causes bias, but right censoring leads to no noteworthy problems. Extensive time aggregation biases estimates of intrinsic propensities but not crosscase influences. Importantly, random sampling can yield good results on diffusion processes if there are supplementary data on influential cases outside the sample. The capability of obtaining good estimates from sampled diffusion histories should help to advance research on diffusion.
CONNECTIONS 18(1):10410 ©1995 INSNA Commentary: Sampling in Social Networks
"... In classic statistical theory, if a random sample is drawn from a population whose underlying distribution is known, it may be assumed that the properties of the sample mirror those of the population (Snedecor and Cochran, 1972). On that cornerstone is built a statistical superstructure that permits ..."
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In classic statistical theory, if a random sample is drawn from a population whose underlying distribution is known, it may be assumed that the properties of the sample mirror those of the population (Snedecor and Cochran, 1972). On that cornerstone is built a statistical superstructure that permits estimation, hypothesis testing, assurance of internal validity, generalizability, and modeling. For a variety of actual sampling schemes — simple random, stratified, probability proportional to size, systematic, cluster, multistage — considerable mathematical work has established appropriate point estimate and variance formulas, and has defined the potential for bias and other threats to validity (Levy and Lemeshow, 1980). This body of work provides satisfying precision for the estimation of uncertainty in defining population characteristics. Random Graphs In the field of network analysis, sampling theory has been associated with defining the mathematical properties of random graphs. Though others preceded them, Erdos and Renyi (1959, 1960) are credited with establishing the theoretical base for estimation of such properties. During the past several decades considerable effort has been invested in describing graphs, and many familiar properties of social network have been established for random graphs. Investigators have explored the mean and variance of degree in a graph (Frank, 1980; Rapoport, 1979a); the probability that a graph will be connected (Gilbert, 1959); the distribution of connected components in a graph (Frank, 1978a; Ling, 1975; Naus and Rabinowitz, 1975); and general types of estimation in large graphs under various sampling schemes (Frank, 1980, 1981, 1978b) One specific type of network investigation — snowball sampling (Goodman, 1961) —
International Journal of Remote Sensing
"... Performance evaluation of classification trees for building detection from aerial images and lidar data: a comparison of classification trees models ..."
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Performance evaluation of classification trees for building detection from aerial images and lidar data: a comparison of classification trees models
The Structure of Undergraduate Association Networks: A Quantitative Ethnography
"... The challenge of collecting complete associational networks has restricted network studies to small datasets. To deal with larger processes, two general procedures have been developed: the use of indicators such as citation structures or the diffusion of innovations to model human interactions, and ..."
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The challenge of collecting complete associational networks has restricted network studies to small datasets. To deal with larger processes, two general procedures have been developed: the use of indicators such as citation structures or the diffusion of innovations to model human interactions, and limiting the sample of associates ' names. A body of theoretical and empirical work has identified several problems with these methods. We examine a unique solution to these problems—measuring online social networks of college students. In this paper we present an original network dataset of undergraduate Facebook users and demonstrate the feasibility and acceptability of this form of measurement. We conclude with a preliminary exploration of Network Homophily and Multiplexity on Facebook.