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  • 1.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Assessing dependence, independence, and conditional independence2015In: Festschrift in Honor of Hans Nyquist on the Occation of His 65th Birthday / [ed] Ellinor Fackle-Fornius, Stockholm: Stockholm University, 2015, p. 100-115Chapter in book (Refereed)
  • 2.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Network sampling2011In: International Encyclopedia of Statistical Science / [ed] Miodrag Lovric, Berlin: Springer, 2011, p. 404-Chapter in book (Other academic)
  • 3.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Network surveys2010In: Official statistics: methodology and applications in honour of Daniel Thorburn / [ed] Michael Carlson, Hans Nyquist, Mattias Villani, Stockholm: Department of Statistics, Stockholm University , 2010, p. 51-60Chapter in book (Other academic)
  • 4.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Probabilistic network models2011In: International Encyclopedia of Statistical Science / [ed] Miodrag Lovric, Berlin: Springer, 2011, p. 458-Chapter in book (Other academic)
  • 5.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Social Network Analysis, Estimation, and Sampling in2015In: Encyclopedia of Complexity and Systems Science / [ed] Robert A. Meyers, New York: Springer Berlin/Heidelberg, 2015, p. 1-26Chapter in book (Refereed)
  • 6.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Social Network Analysis, Estimation and Sampling in2009In: Encyclopedia of Complexity and Systems Science / [ed] Robert A. Meyers, New York: Springer , 2009, p. 8213-8231Chapter in book (Other academic)
  • 7.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Statistical information tools for multivariate discrete data2011In: Modern Mathematical Tools and Techniques in Capturing Complexity / [ed] Leandro Pardo, Narayanaswamy Balakrishnan, María Ángeles Gil, Berlin Heidelberg: Springer, 2011, p. 177-190Chapter in book (Other academic)
  • 8.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Survey sampling in networks2011In: The SAGE handbook of social network analysis / [ed] John Scott and Peter J. Carrington, London: Sage Publications, 2011, p. 389-403Chapter in book (Other academic)
  • 9.
    Frank, Ove
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Carrington, Peter J.
    Estimation of offending and co-offending using available data with model support2007In: The Journal of mathematical sociology, ISSN 0022-250X, E-ISSN 1545-5874, Vol. 31, no 1, p. 1-46Article in journal (Refereed)
    Abstract [en]

    Police data under-report the numbers of crimes and of offenders, the numbers of offenders participating in individual criminal incidents (incident sizes) and the numbers of incidents in which individual offenders participate (offender activity). Criminal participation in incidents is a concept that underlies and unifies all of these phenomena, so that the numbers of incidents and of offenders, and incident size distributions and offender activity distributions, can all be derived from the criminal participation matrix. Two related probability models are presented that permit the estimation of numbers of incidents and offenders, incident size distributions, offender activity distributions, and co-offending distributions, from police-reported crime data, and data on the reporting of crime to police. The models are estimated, using data from the Canadian Uniform Crime Reporting Survey and national victimization surveys for the period 1995 - 2001.

  • 10.
    Frank, Ove
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Shafie, Termeh
    University of Konstanz, Germany.
    Multigraph Complexity, Data Aggregation, and Statistical Analysis2013Report (Other academic)
  • 11.
    Frank, Ove
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Shafie, Termeh
    Multivariate Entropy Analysis of Network Data2016In: Bulletin of Sociological Methodology, ISSN 0759-1063, E-ISSN 2070-2779, Vol. 129, p. 45-63Article in journal (Refereed)
    Abstract [en]

    Multigraphs with numerical or qualitative attributes defined on vertices and edges can benefit from systematic methods based on multivariate entropies for describing and analysing the interdependencies that are present between vertex and edge attributes. This is here illustrated by application of these tools to a subset of data on the social relations among Renaissance Florentine families collected by John Padgett. Using multivariate entropies we show how it is possible to systematically check for tendencies in data that can be described as independencies or conditional independencies, or as dependencies allowing certain combinations of variables to predict other variables. We also show how different structural models can be tested by divergence measures obtained from the multivariate entropies.

  • 12.
    Frank, Ove
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Shafie, Termeh
    Random multigraphs and aggregated triads with fixed degrees2018In: Network Science, ISSN 2050-1242, Vol. 6, no 2, p. 232-250Article in journal (Refereed)
    Abstract [en]

    Random multigraphs with fixed degrees are obtained by the configuration model or by so called random stub matching. New combinatorial results are given for the global probability distribution of edge multiplicities and its marginal local distributions of loops and edges. The number of multigraphs on triads is determined for arbitrary degrees, and aggregated triads are shown to be useful for analyzing regular and almost regular multigraphs. Relationships between entropy and complexity are given and numerically illustrated for multigraphs with different number of vertices and specified average and variance for the degrees.

  • 13.
    Shafie, Termeh
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Complexity of Families of Multigraphs2012In: 2012 JSM Proceedings: Papers Presented at the Joint Statistical Meetings, San Diego, California, July 28-August 2, 2012, and Other ASA-sponsored Conferences, American Statistical Association , 2012Conference paper (Refereed)
    Abstract [en]

    This article describes families of finite multigraphs with labeled or unlabeled edges and vertices. It shows how size and complexity vary for different types of equivalence classes of graphs defined by ignoring only edge labels or ignoring both edge and vertex labels. Complexity is quantified by the distribution of edge multiplicities, and different complexity measures are discussed. Basic occupancy models for multigraphs are used to illustrate different graph distributions on isomorphism and complexity. The loss of information caused by ignoring edge and vertex labels is quantified by entropy and joint information that provide tools for studying properties of and relations between different graph families.

  • 14.
    Shafie, Termeh
    et al.
    University of Konstanz, Germany.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Estimation of global network properties by using local aggregated data.2014Conference paper (Other academic)
    Abstract [en]

    Estimation of global network properties by using local aggregated data.   We consider networks that can be modeled as random multigraphs and indicate various applications and appearances of multigraphs. Global properties are measured by entropy and complexity based on the edge multiplicities. Sample data give partial information about the network, and we consider a special kind of local information given by aggregation of vertices and counting of edges within and between blocks of vertices. Shafie (2012) gives a comprehensive analysis and comparison of random multigraphs of different kinds, and Frank and Shafie (2013) present special results about complexity and data aggregation in multigraphs.  

  • 15. Shafie, Termeh
    et al.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Evaluating network centrality using entropy tools2016In: XXXVI International Sunbelt Social Network Conference: Presentation and Poster Abstract, 2016, p. 195-196Conference paper (Other academic)
    Abstract [en]

    We recently introduced a new way of using statistical entropies to capture interdependencies among vertex and edge variables in multivariate networks. These entropies are used to systematically check for tendencies in the multidimensional variable set concerning redundancies, functional relationships, independencies and conditional independencies among different variable combinations. An important use of this technique is to apply it for selection of good summary measures of network structure. For instance, there are many alternative network statistics available for measuring centrality, and it is not always easy to decide which one is appropriate for a current application. In this presentation, we consider different centrality statistics among the variables in the analysis. By using univariate and multivariate entropies, we aim to find the centrality measure that is most relevant for the network property of interest. Throughout this presentation, we use John Padgett’s extended Florentine network data consisting of 87 families where the vertices as well as the edges have numerical or qualitative attributes defined on them. We create edge and vertex variables that capture network information via the most common centrality measures. The dependence structure of the variables is then explored by entropy analysis and it is determined which centrality measure is most appropriate for representing political, social or economic influence among the Florentine families. Further, we demonstrate how divergence measures can be used to indicate and test structural tendencies with respect to centrality in this network.

  • 16. Shafie, Termeh
    et al.
    Frank, Ove
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Finding informative triads2017Conference paper (Other academic)
    Abstract [en]

    Network data containing attributes of both vertices and vertex pairs can be analysed together using multivariate entropies. This general multivariate technique is here illustrated with data previously analysed by Lazega and others. We focus here on the role of triad count statistics for exploring the dependency structure of the network attributes.

1 - 16 of 16
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