5,643 research outputs found
On Facebook, most ties are weak
Pervasive socio-technical networks bring new conceptual and technological
challenges to developers and users alike. A central research theme is
evaluation of the intensity of relations linking users and how they facilitate
communication and the spread of information. These aspects of human
relationships have been studied extensively in the social sciences under the
framework of the "strength of weak ties" theory proposed by Mark Granovetter.13
Some research has considered whether that theory can be extended to online
social networks like Facebook, suggesting interaction data can be used to
predict the strength of ties. The approaches being used require handling
user-generated data that is often not publicly available due to privacy
concerns. Here, we propose an alternative definition of weak and strong ties
that requires knowledge of only the topology of the social network (such as who
is a friend of whom on Facebook), relying on the fact that online social
networks, or OSNs, tend to fragment into communities. We thus suggest
classifying as weak ties those edges linking individuals belonging to different
communities and strong ties as those connecting users in the same community. We
tested this definition on a large network representing part of the Facebook
social graph and studied how weak and strong ties affect the
information-diffusion process. Our findings suggest individuals in OSNs
self-organize to create well-connected communities, while weak ties yield
cohesion and optimize the coverage of information spread.Comment: Accepted version of the manuscript before ACM editorial work. Check
http://cacm.acm.org/magazines/2014/11/179820-on-facebook-most-ties-are-weak/
for the final versio
Analysis of a large-scale weighted network of one-to-one human communication
We construct a connected network of 3.9 million nodes from mobile phone call
records, which can be regarded as a proxy for the underlying human
communication network at the societal level. We assign two weights on each edge
to reflect the strength of social interaction, which are the aggregate call
duration and the cumulative number of calls placed between the individuals over
a period of 18 weeks. We present a detailed analysis of this weighted network
by examining its degree, strength, and weight distributions, as well as its
topological assortativity and weighted assortativity, clustering and weighted
clustering, together with correlations between these quantities. We give an
account of motif intensity and coherence distributions and compare them to a
randomized reference system. We also use the concept of link overlap to measure
the number of common neighbors any two adjacent nodes have, which serves as a
useful local measure for identifying the interconnectedness of communities. We
report a positive correlation between the overlap and weight of a link, thus
providing strong quantitative evidence for the weak ties hypothesis, a central
concept in social network analysis. The percolation properties of the network
are found to depend on the type and order of removed links, and they can help
understand how the local structure of the network manifests itself at the
global level. We hope that our results will contribute to modeling weighted
large-scale social networks, and believe that the systematic approach followed
here can be adopted to study other weighted networks.Comment: 25 pages, 17 figures, 2 table
Bridgeness: A Local Index on Edge Significance in Maintaining Global Connectivity
Edges in a network can be divided into two kinds according to their different
roles: some enhance the locality like the ones inside a cluster while others
contribute to the global connectivity like the ones connecting two clusters. A
recent study by Onnela et al uncovered the weak ties effects in mobile
communication. In this article, we provide complementary results on document
networks, that is, the edges connecting less similar nodes in content are more
significant in maintaining the global connectivity. We propose an index named
bridgeness to quantify the edge significance in maintaining connectivity, which
only depends on local information of network topology. We compare the
bridgeness with content similarity and some other structural indices according
to an edge percolation process. Experimental results on document networks show
that the bridgeness outperforms content similarity in characterizing the edge
significance. Furthermore, extensive numerical results on disparate networks
indicate that the bridgeness is also better than some well-known indices on
edge significance, including the Jaccard coefficient, degree product and
betweenness centrality.Comment: 10 pages, 4 figures, 1 tabl
Triangles to Capture Social Cohesion
Although community detection has drawn tremendous amount of attention across
the sciences in the past decades, no formal consensus has been reached on the
very nature of what qualifies a community as such. In this article we take an
orthogonal approach by introducing a novel point of view to the problem of
overlapping communities. Instead of quantifying the quality of a set of
communities, we choose to focus on the intrinsic community-ness of one given
set of nodes. To do so, we propose a general metric on graphs, the cohesion,
based on counting triangles and inspired by well established sociological
considerations. The model has been validated through a large-scale online
experiment called Fellows in which users were able to compute their social
groups on Face- book and rate the quality of the obtained groups. By observing
those ratings in relation to the cohesion we assess that the cohesion is a
strong indicator of users subjective perception of the community-ness of a set
of people
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e-Governance: Supporting pragmatic direct deliberative action through online communities of interest
Authors often report on the limited success of e-Government initiatives in developing nations. Top down, national strategies are developed to target improved government services, but maintain hierarchical, citizen-state conceptions of governance through representative democracy. An alternative conception, direct deliberative democracy, frames the potential role of the internet in governance differently. Web based platforms might support locally animated deliberations, which target pragmatic outcomes, while the resulting social networks afford collective learning through connections across traditional boundaries. This paper presents an investigation of direct deliberative governance as it occurs in online 'communities of interest', and is based on research with such a community in southern Africa. We investigate contributions to the online governance process and develop an action typology distinguishing between degrees of 'agency freedom'. Network analytic techniques are then used to understand how acts of varying degree are expressed in terms of the structure of a social network. The aim, more broadly, is to understand how the environment shapes acts of direct deliberative governance, and, in turn, how the acts shape the evolution and effectiveness of the community. The preliminary results suggest design considerations for online governance communities, and highlight their role to not only provide deliberative space, but to mediate social network connections
Finding influential spreaders from human activity beyond network location
Most centralities proposed for identifying influential spreaders on social
networks to either spread a message or to stop an epidemic require the full
topological information of the network on which spreading occurs. In practice,
however, collecting all connections between agents in social networks can be
hardly achieved. As a result, such metrics could be difficult to apply to real
social networks. Consequently, a new approach for identifying influential
people without the explicit network information is demanded in order to provide
an efficient immunization or spreading strategy, in a practical sense. In this
study, we seek a possible way for finding influential spreaders by using the
social mechanisms of how social connections are formed in real networks. We
find that a reliable immunization scheme can be achieved by asking people how
they interact with each other. From these surveys we find that the
probabilistic tendency to connect to a hub has the strongest predictive power
for influential spreaders among tested social mechanisms. Our observation also
suggests that people who connect different communities is more likely to be an
influential spreader when a network has a strong modular structure. Our finding
implies that not only the effect of network location but also the behavior of
individuals is important to design optimal immunization or spreading schemes
Community Detection in Dynamic Networks via Adaptive Label Propagation
An adaptive label propagation algorithm (ALPA) is proposed to detect and
monitor communities in dynamic networks. Unlike the traditional methods by
re-computing the whole community decomposition after each modification of the
network, ALPA takes into account the information of historical communities and
updates its solution according to the network modifications via a local label
propagation process, which generally affects only a small portion of the
network. This makes it respond to network changes at low computational cost.
The effectiveness of ALPA has been tested on both synthetic and real-world
networks, which shows that it can successfully identify and track dynamic
communities. Moreover, ALPA could detect communities with high quality and
accuracy compared to other methods. Therefore, being low-complexity and
parameter-free, ALPA is a scalable and promising solution for some real-world
applications of community detection in dynamic networks.Comment: 16 pages, 11 figure
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