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Subject: [bms-rc33] Journal - Network Analysis Special Issue (Advances in Data Analysis & Classification) (fwd)
From: Barry Wellman <[log in to unmask]>
Reply-To:Barry Wellman <[log in to unmask]>
Date:Mon, 12 Sep 2011 09:09:23 -0400

TEXT/PLAIN (140 lines)

*****  To join INSNA, visit  *****

  Barry Wellman

   S.D. Clark Professor of Sociology, FRSC               NetLab Director
   Department of Sociology                  725 Spadina Avenue, Room 388
   University of Toronto   Toronto Canada M5S 2J4   twitter:barrywellman             fax:+1-416-978-3963
   Updating history:

---------- Forwarded message ----------
Date: Mon, 12 Sep 2011 14:37:27 +0200
From: [log in to unmask]
Reply-To: [log in to unmask], [log in to unmask]
To: [log in to unmask], [log in to unmask]
Subject: [bms-rc33] Journal - Network Analysis Special Issue (Advances in Data
     Analysis & Classification)

>> Consider submitting your methodologically interesting articles to the BMS <<

Advances in Data Analysis and Classification
Volume 5, Number 2 / July 2011

Special issue on ‘Network Analysis’ Preface by the Guest Editors,
by Anuška Ferligoj and Vladimir Batagelj (pp. 77-79)

Network ensemble clustering using latent roles
by Ulrik Brandes, Jürgen Lerner and Uwe Nagel (pp. 81-94):
We present a clustering method for collections of graphs based on the
assumptions that graphs in the same cluster have a similar role structure and
that the respective roles can be founded on implicit vertex types. Given a
network ensemble (a collection of attributed graphs with some substantive
commonality), we start by partitioning the set of all vertices based on
attribute similarity. Projection of each graph onto the resulting vertex types
yields feature vectors of equal dimensionality, irrespective of the original
graph sizes. These feature vectors are then subjected to standard clustering
methods. This approach is motivated by social network concepts, and we
demonstrate its utility on an ensemble of personal networks of migrants, where
we extract structurally similar groups and show their resemblance to predicted
acculturation strategies.

On the use of external information in social network analysis,
by Giuseppe Giordano and Maria Prosperina Vitale (pp. 95-112):
Network analysis focuses on links among interacting units (actors). Interactions
are often derived from the presence of actors at events or activities (two-mode
network) and this information is coded and arranged in a typical affiliation
matrix. In addition to the relational data, interest may focus on external
information gathered on both actors and events. Our aim is to explore the
effect of external information on the formation of ties by setting a strategy
able to decompose the original affiliation matrix by linear combinations of
data vectors representing external information with a suitable matrix of
coefficients. This allows to obtain peculiar relational data matrices that
include the effect of external information. The derived adjacency matrices can
then be analyzed from the network analysis perspective. In particular, we look
for groups of structurally equivalent actors obtained through clustering
methods. Illustrative examples and a real dataset in the framework of
scientific collaboration will give a major insight into the proposed strategy.

Web page importance ranking,
Wolfgang Gaul (pp. 113-128):
An approach is proposed that uses a set of interesting Web pages as starting
point for a minimum walk algorithm to provide recommendations of additionally
important Web information within a m-clicks-ahead situation. A discussion of
known page importance ranking techniques as well as examples of the application
of the new algorithm show that Web link structure dependent approaches should be
enriched by considerations as to how the analysis of additional data and the use
of suited support tools can be incorporated. These considerations include
aspects as, e.g., personalization, query dependence and topic sensitivity of
the underlying pages, the dynamic nature of the Web, as well as the possibility
to perform calculations online.

Fast algorithms for determining (generalized) core groups in social networks,
by Vladimir Batagelj and Matjaž Zaveršnik (pp. 129-145):
The structure of a large network (graph) can often be revealed by partitioning
it into smaller and possibly more dense sub-networks that are easier to handle.
One of such decompositions is based on “k-cores”, proposed in 1983 by Seidman.
Together with connectivity components, cores are one among few concepts that
provide efficient decompositions of large graphs and networks. In this paper we
propose an efficient algorithm for determining the cores decomposition of a
given network with complexity O(m), where m is the number of lines (edges or
arcs). In the second part of the paper the classical concept of k-core is
generalized in a way that uses a vertex property function instead of degree of
a vertex. For local monotone vertex property functions the corresponding
generalized cores can be determined in O(m·max(D,logn)) time, where n is the
number of vertices and &#916; is the maximum degree. Finally the proposed
algorithms are illustrated by the analysis of a collaboration network in the
field of computational geometry.

Assessing and accounting for time heterogeneity in stochastic actor oriented
by Joshua A. Lospinoso, Michael Schweinberger, Tom A. B. Snijders and Ruth M.
Ripley (pp. 147-176):
This paper explores time heterogeneity in stochastic actor oriented models
(SAOM) proposed by Snijders (Sociological methodology. Blackwell, Boston, pp
361–395, 2001) which are meant to study the evolution of networks. SAOMs model
social networks as directed graphs with nodes representing people,
organizations, etc., and dichotomous relations representing underlying
relationships of friendship, advice, etc. We illustrate several reasons why
heterogeneity should be statistically tested and provide a fast, convenient
method for assessment and model correction. SAOMs provide a flexible framework
for network dynamics which allow a researcher to test selection, influence,
behavioral, and structural properties in network data over time. We show how
the forward-selecting, score type test proposed by Schweinberger (Chapter 4:
Statistical modeling of network panel data: goodness of fit. PhD thesis,
University of Groningen 2007) can be employed to quickly assess heterogeneity
at almost no additional computational cost. One step estimates are used to
assess the magnitude of the heterogeneity. Simulation studies are conducted to
support the validity of this approach. The ASSIST dataset (Campbell et al. In
Lancet 371(9624):1595–1602, 2008) is reanalyzed with the score type test, one
step estimators, and a full estimation for illustration. These tools are
implemented in the RSiena package, and a brief walkthrough is provided.

* Karl M. van Meter                   BMS, Bulletin de Methodologie Sociologique
* [log in to unmask]               (Bulletin of Sociological Methodology)
* tel 33 (0)1 40 51 85 19   
*                      distribution list [log in to unmask]
*                      free distribution list [log in to unmask]
* BMS-AIMS                            RC33, Research Committee Logic &
* 45 rue Linné                        Methodology of the International
* 75005 Paris, France                 Sociological Association (ISA)

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