19,725 research outputs found
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
Measuring social dynamics in a massive multiplayer online game
Quantification of human group-behavior has so far defied an empirical,
falsifiable approach. This is due to tremendous difficulties in data
acquisition of social systems. Massive multiplayer online games (MMOG) provide
a fascinating new way of observing hundreds of thousands of simultaneously
socially interacting individuals engaged in virtual economic activities. We
have compiled a data set consisting of practically all actions of all players
over a period of three years from a MMOG played by 300,000 people. This
large-scale data set of a socio-economic unit contains all social and economic
data from a single and coherent source. Players have to generate a virtual
income through economic activities to `survive' and are typically engaged in a
multitude of social activities offered within the game. Our analysis of
high-frequency log files focuses on three types of social networks, and tests a
series of social-dynamics hypotheses. In particular we study the structure and
dynamics of friend-, enemy- and communication networks. We find striking
differences in topological structure between positive (friend) and negative
(enemy) tie networks. All networks confirm the recently observed phenomenon of
network densification. We propose two approximate social laws in communication
networks, the first expressing betweenness centrality as the inverse square of
the overlap, the second relating communication strength to the cube of the
overlap. These empirical laws provide strong quantitative evidence for the Weak
ties hypothesis of Granovetter. Further, the analysis of triad significance
profiles validates well-established assertions from social balance theory. We
find overrepresentation (underrepresentation) of complete (incomplete) triads
in networks of positive ties, and vice versa for networks of negative ties...Comment: 23 pages 19 figure
Multirelational Organization of Large-scale Social Networks in an Online World
The capacity to collect fingerprints of individuals in online media has
revolutionized the way researchers explore human society. Social systems can be
seen as a non-linear superposition of a multitude of complex social networks,
where nodes represent individuals and links capture a variety of different
social relations. Much emphasis has been put on the network topology of social
interactions, however, the multi-dimensional nature of these interactions has
largely been ignored in empirical studies, mostly because of lack of data.
Here, for the first time, we analyze a complete, multi-relational, large social
network of a society consisting of the 300,000 odd players of a massive
multiplayer online game. We extract networks of six different types of
one-to-one interactions between the players. Three of them carry a positive
connotation (friendship, communication, trade), three a negative (enmity, armed
aggression, punishment). We first analyze these types of networks as separate
entities and find that negative interactions differ from positive interactions
by their lower reciprocity, weaker clustering and fatter-tail degree
distribution. We then proceed to explore how the inter-dependence of different
network types determines the organization of the social system. In particular
we study correlations and overlap between different types of links and
demonstrate the tendency of individuals to play different roles in different
networks. As a demonstration of the power of the approach we present the first
empirical large-scale verification of the long-standing structural balance
theory, by focusing on the specific multiplex network of friendship and enmity
relations.Comment: 7 pages, 5 figures, accepted for publication in PNA
On the Question of Effective Sample Size in Network Modeling: An Asymptotic Inquiry
The modeling and analysis of networks and network data has seen an explosion
of interest in recent years and represents an exciting direction for potential
growth in statistics. Despite the already substantial amount of work done in
this area to date by researchers from various disciplines, however, there
remain many questions of a decidedly foundational nature - natural analogues of
standard questions already posed and addressed in more classical areas of
statistics - that have yet to even be posed, much less addressed. Here we raise
and consider one such question in connection with network modeling.
Specifically, we ask, "Given an observed network, what is the sample size?"
Using simple, illustrative examples from the class of exponential random graph
models, we show that the answer to this question can very much depend on basic
properties of the networks expected under the model, as the number of vertices
in the network grows. In particular, adopting the (asymptotic) scaling of
the variance of the maximum likelihood parameter estimates as a notion of
effective sample size (), we show that when modeling the
overall propensity to have ties and the propensity to reciprocate ties, whether
the networks are sparse or not under the model (i.e., having a constant or an
increasing number of ties per vertex, respectively) is sufficient to yield an
order of magnitude difference in , from to
. In addition, we report simulation study results that suggest
similar properties for models for triadic (friend-of-a-friend) effects. We then
explore some practical implications of this result, using both simulation and
data on food-sharing from Lamalera, Indonesia.Comment: Published at http://dx.doi.org/10.1214/14-STS502 in the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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