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fyi

Barry Wellman


Step by step, link by link, putting it together--Streisand/Sondheim
The earth to be spannd, connected by network--Walt Whitman
It's Always Something--Roseanne Roseannadanna
Go Raptors Go
A day like all days, filled with those events
that alter and illuminate our times--Walter Cronkite
_______________________________________________________________________
Director, NetLab Network FRSC
Distinguished Visiting Scholar Social Media Lab Ryerson University
Founder, International Network for Social Network Analysis
NETWORKED: The New Social Operating System Lee Rainie & Barry Wellman
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_______________________________________________________________________


---------- Forwarded message ----------
Date: Thu, 06 Jun 2019 16:20:32 +0200
From: VAN METER Karl <[log in to unmask]>
Reply-To: [log in to unmask], VAN METER Karl <[log in to unmask]>
To: [log in to unmask], [log in to unmask]
Cc: [log in to unmask]
Subject: [bms-rc33] Meeting - How to Analyze Large-Scale Network Structures
(13-14 Jun, Mannheim DE)

Thanks to Sebastian Pink

Deadline: 23 April 2019


Dear BMS mailing list members,

We are organizing a workshop named "How to Analyze Large-Scale Network
Structures? The 'Boundary Specification Problem' Revisited" held at
13.-14.06.2019 at Mannheim University, Germany.
https://urldefense.proofpoint.com/v2/url?u=https-3A__bit.ly_2Vl8AKH&d=DwIFAw&c=sJ6xIWYx-zLMB3EPkvcnVg&r=yQQsvTNAnbvDXGM4nDrXAje4pr0qHX2qIOcCQtJ5k3w&m=wQ-XRZsmxSHVlNtWjOMxAUfN57mVJ5CMG_sRCE996W4&s=XwIrjC3TRT9WYDbv9ZikQ4boEKmSMrs0_29FozWndB4&e= .

We look forward to seeing you at the University of Mannheim.

Best wishes from Mannheim,

Philipp Brandt and Sebastian Pink

-----

How to Analyze Large-Scale Network Structures?

The Boundary Specification Problem Revisited

Social network analysis presupposes the specification of a network boundary.
Typically, boundaries
follow theoretical definitions of medium-sized groups, think of local elites,
or clear institutional
settings such as school classes, villages, or workplaces. Researchers from
various scientific disciplines
have developed sophisticated statistical methods to analyze the genesis and
evolution of such
networks. These methods include Autoregressive Models, Exponential Random Graph
Models, Latent
Space Models, or Stochastic Actor-Oriented Models.

Over the last decade, however, the size of networks that scholars may use to
answer their research
questions has increased tremendously, and therefore specifying their boundaries
is not as
straightforward anymore. Large relational structures such as Twitter or
Facebook, as well as patent,
citation, covert, transaction, or academic collaboration networks confront
researchers with novel
challenges: their size exceeds typical social networks and stretches the
boundary assumption of
sophisticated methods of social network analysis. At the same time, these
networks have drawn
attention from computational sciences. One promising solution to novel
challenges may be to
integrate computational and sociological approaches and partition large
networks into smaller
segments (providing meaningful sub-network boundaries), analyze them
separately, and then
combine the results back to the larger network to learn about its genesis and
evolution.

This workshop aims to explore how we may use modern computational tools to
partition large-scale
networks into meaningful components and the implications for applying
statistical models to them.
These analytical steps come with empirical and conceptual caveats and require
rigorous descriptions
and substantive interpretations of the sub-components with respect to their
members and the tiegenerating processes. This endeavor is inherently
interdisciplinary and will profit enormously from
joining forces across scientific disciplines, which is why we explicitly invite
interested scholars from a
broad range of fields such as mathematics, physics, biology, environmental
science, computer
science, data science, medicine, criminology, political science, and sociology.

Open questions that scholars interested in these problems will be addressing
include:

Mathematical approaches to partitioning graphs into clusters and community
detection
Probabilistic assignment of members of a sub-network into multiple clusters
according to
different partitioning techniques
Ontological problems of using algorithms to define network boundaries
Techniques for comparing different clusters to each other to arrive at a
substantive
understanding about the dimensions along which the clusters differ
Aggregation of the cluster-based analyses
Computational demands of handling large graphs in general and partitioning
graphs in
particular
Theoretical and conceptual issues of combining partitioning algorithms for
large-scale
networks with actor-oriented statistical models

We hope this workshop will help participants with their challenges and produce
a more general guide
for scholars interested in analyzing large-scale networks. As part of the
workshop, we will discuss ways
for publishing the results to share them with the wider network science
community.

Organizational information

Date: 13.-14.06.2019

Location:

Mannheim Centre for European Social Research
University of Mannheim,
A5, 6, 68159 Mannheim, Germany

Mode:

Oral presentation of 20 minutes and 10 minutes discussion per presenter
Application: Please send a one-page abstract of your topic until the 23.04.2019
to
[log in to unmask] and/or [log in to unmask]

Support: The workshop is supported by the Academy for Sociology and the
Mannheim Centre
for European Social Research. In addition to providing refreshments and a
conference dinner, we are able to cover the accommodation for a limited number
of
participants who have no own funding. Please indicate in your application if
you
would like to be considered.

If you have any questions, please do not hesitate to ask us!

We look forward to seeing you at the University of Mannheim,

Philipp Brandt and Sebastian Pink

>> Consider submitting your methodologically interesting articles to the BMS <<
>> Soumettez vos articles méthodologiquement intéressants au BMS <<
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