11,303 research outputs found
Emergence of scale-free close-knit friendship structure in online social networks
Despite the structural properties of online social networks have attracted
much attention, the properties of the close-knit friendship structures remain
an important question. Here, we mainly focus on how these mesoscale structures
are affected by the local and global structural properties. Analyzing the data
of four large-scale online social networks reveals several common structural
properties. It is found that not only the local structures given by the
indegree, outdegree, and reciprocal degree distributions follow a similar
scaling behavior, the mesoscale structures represented by the distributions of
close-knit friendship structures also exhibit a similar scaling law. The degree
correlation is very weak over a wide range of the degrees. We propose a simple
directed network model that captures the observed properties. The model
incorporates two mechanisms: reciprocation and preferential attachment. Through
rate equation analysis of our model, the local-scale and mesoscale structural
properties are derived. In the local-scale, the same scaling behavior of
indegree and outdegree distributions stems from indegree and outdegree of nodes
both growing as the same function of the introduction time, and the reciprocal
degree distribution also shows the same power-law due to the linear
relationship between the reciprocal degree and in/outdegree of nodes. In the
mesoscale, the distributions of four closed triples representing close-knit
friendship structures are found to exhibit identical power-laws, a behavior
attributed to the negligible degree correlations. Intriguingly, all the
power-law exponents of the distributions in the local-scale and mesoscale
depend only on one global parameter -- the mean in/outdegree, while both the
mean in/outdegree and the reciprocity together determine the ratio of the
reciprocal degree of a node to its in/outdegree.Comment: 48 pages, 34 figure
Social Bootstrapping: How Pinterest and Last.fm Social Communities Benefit by Borrowing Links from Facebook
How does one develop a new online community that is highly engaging to each
user and promotes social interaction? A number of websites offer friend-finding
features that help users bootstrap social networks on the website by copying
links from an established network like Facebook or Twitter. This paper
quantifies the extent to which such social bootstrapping is effective in
enhancing a social experience of the website. First, we develop a stylised
analytical model that suggests that copying tends to produce a giant connected
component (i.e., a connected community) quickly and preserves properties such
as reciprocity and clustering, up to a linear multiplicative factor. Second, we
use data from two websites, Pinterest and Last.fm, to empirically compare the
subgraph of links copied from Facebook to links created natively. We find that
the copied subgraph has a giant component, higher reciprocity and clustering,
and confirm that the copied connections see higher social interactions.
However, the need for copying diminishes as users become more active and
influential. Such users tend to create links natively on the website, to users
who are more similar to them than their Facebook friends. Our findings give new
insights into understanding how bootstrapping from established social networks
can help engage new users by enhancing social interactivity.Comment: Proc. 23rd International World Wide Web Conference (WWW), 201
Strong-Tie Social Connections Versus Weak-Tie Social Connections
Discussions regarding the strength of social ties relate to social capital theory. As Robert Putnam describes it, social capital theory suggests that social networks have value at the micro (individual), meso (community), and macro (societal) levels. An individual\u27s social network is comprised of multiple, multiplex social ties of varying strengths. Strong ties exist among individuals connected within densely knit, homogenous networks such as those involving kin and close friends. Weak ties exist among individuals connected within sparse, heterogeneous networks such as those involving acquaintances
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
A natural experiment of social network formation and dynamics
10.1073/pnas.1404770112Proceedings of the National Academy of Sciences of the United States of America112216595-660
An exploration of concepts of community through a case study of UK university web production
The paper explores the inter-relation and differences between the concepts of occupational community, community of practice, online community and social network. It uses as a case study illustration the domain of UK university web site production and specifically a listserv for those involved in it. Different latent occupational communities are explored, and the potential for the listserv to help realize these as an active sense of community is considered. The listserv is not (for most participants) a tight knit community of practice, indeed it fails many criteria for an online community. It is perhaps best conceived as a loose knit network of practice, valued for information, implicit support and for the maintenance of weak ties. Through the analysis the case for using strict definitions of the theoretical concepts is made
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