824 research outputs found
Growing Attributed Networks through Local Processes
This paper proposes an attributed network growth model. Despite the knowledge
that individuals use limited resources to form connections to similar others,
we lack an understanding of how local and resource-constrained mechanisms
explain the emergence of rich structural properties found in real-world
networks. We make three contributions. First, we propose a parsimonious and
accurate model of attributed network growth that jointly explains the emergence
of in-degree distributions, local clustering, clustering-degree relationship
and attribute mixing patterns. Second, our model is based on biased random
walks and uses local processes to form edges without recourse to global network
information. Third, we account for multiple sociological phenomena: bounded
rationality, structural constraints, triadic closure, attribute homophily, and
preferential attachment. Our experiments indicate that the proposed Attributed
Random Walk (ARW) model accurately preserves network structure and attribute
mixing patterns of six real-world networks; it improves upon the performance of
eight state-of-the-art models by a statistically significant margin of 2.5-10x.Comment: 11 pages, 13 figure
Stochastic network formation and homophily
This is a chapter of the forthcoming Oxford Handbook on the Economics of
Networks
A new hierarchical clustering algorithm to identify non-overlapping like-minded communities
A network has a non-overlapping community structure if the nodes of the
network can be partitioned into disjoint sets such that each node in a set is
densely connected to other nodes inside the set and sparsely connected to the
nodes out- side it. There are many metrics to validate the efficacy of such a
structure, such as clustering coefficient, betweenness, centrality, modularity
and like-mindedness. Many methods have been proposed to optimize some of these
metrics, but none of these works well on the recently introduced metric
like-mindedness. To solve this problem, we propose a be- havioral property
based algorithm to identify communities that optimize the like-mindedness
metric and compare its performance on this metric with other behavioral data
based methodologies as well as community detection methods that rely only on
structural data. We execute these algorithms on real-life datasets of
Filmtipset and Twitter and show that our algorithm performs better than the
existing algorithms with respect to the like-mindedness metric
Link creation and profile alignment in the aNobii social network
The present work investigates the structural and dynamical properties of
aNobii\footnote{http://www.anobii.com/}, a social bookmarking system designed
for readers and book lovers. Users of aNobii provide information about their
library, reading interests and geographical location, and they can establish
typed social links to other users. Here, we perform an in-depth analysis of the
system's social network and its interplay with users' profiles. We describe the
relation of geographic and interest-based factors to social linking.
Furthermore, we perform a longitudinal analysis to investigate the interplay of
profile similarity and link creation in the social network, with a focus on
triangle closure. We report a reciprocal causal connection: profile similarity
of users drives the subsequent closure in the social network and, reciprocally,
closure in the social network induces subsequent profile alignment. Access to
the dynamics of the social network also allows us to measure quantitative
indicators of preferential linking.Comment: http://www.iisocialcom.org/conference/socialcom2010
Follow Whom? Chinese Users Have Different Choice
Sina Weibo, which was launched in 2009, is the most popular Chinese
micro-blogging service. It has been reported that Sina Weibo has more than 400
million registered users by the end of the third quarter in 2012. Sina Weibo
and Twitter have a lot in common, however, in terms of the following
preference, Sina Weibo users, most of whom are Chinese, behave differently
compared with those of Twitter.
This work is based on a data set of Sina Weibo which contains 80.8 million
users' profiles and 7.2 billion relations and a large data set of Twitter.
Firstly some basic features of Sina Weibo and Twitter are analyzed such as
degree and activeness distribution, correlation between degree and activeness,
and the degree of separation. Then the following preference is investigated by
studying the assortative mixing, friend similarities, following distribution,
edge balance ratio, and ranking correlation, where edge balance ratio is newly
proposed to measure balance property of graphs. It is found that Sina Weibo has
a lower reciprocity rate, more positive balanced relations and is more
disassortative. Coinciding with Asian traditional culture, the following
preference of Sina Weibo users is more concentrated and hierarchical: they are
more likely to follow people at higher or the same social levels and less
likely to follow people lower than themselves. In contrast, the same kind of
following preference is weaker in Twitter. Twitter users are open as they
follow people from levels, which accords with its global characteristic and the
prevalence of western civilization. The message forwarding behavior is studied
by displaying the propagation levels, delays, and critical users. The following
preference derives from not only the usage habits but also underlying reasons
such as personalities and social moralities that is worthy of future research.Comment: 9 pages, 13 figure
Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns
Many investigations of scientific collaboration are based on statistical
analyses of large networks constructed from bibliographic repositories. These
investigations often rely on a wealth of bibliographic data, but very little or
no other information about the individuals in the network, and thus, fail to
illustrate the broader social and academic landscape in which collaboration
takes place. In this article, we perform an in-depth longitudinal analysis of a
relatively small network of scientific collaboration (N = 291) constructed from
the bibliographic record of a research center involved in the development and
application of sensor network and wireless technologies. We perform a
preliminary analysis of selected structural properties of the network,
computing its range, configuration and topology. We then support our
preliminary statistical analysis with an in-depth temporal investigation of the
assortative mixing of selected node characteristics, unveiling the researchers'
propensity to collaborate preferentially with others with a similar academic
profile. Our qualitative analysis of mixing patterns offers clues as to the
nature of the scientific community being modeled in relation to its
organizational, disciplinary, institutional, and international arrangements of
collaboration.Comment: Scientometrics (In press
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
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