1,720 research outputs found
Happiness is assortative in online social networks
Social networks tend to disproportionally favor connections between
individuals with either similar or dissimilar characteristics. This propensity,
referred to as assortative mixing or homophily, is expressed as the correlation
between attribute values of nearest neighbour vertices in a graph. Recent
results indicate that beyond demographic features such as age, sex and race,
even psychological states such as "loneliness" can be assortative in a social
network. In spite of the increasing societal importance of online social
networks it is unknown whether assortative mixing of psychological states takes
place in situations where social ties are mediated solely by online networking
services in the absence of physical contact. Here, we show that general
happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6
month record of their individual tweets, is indeed assortative across the
Twitter social network. To our knowledge this is the first result that shows
assortative mixing in online networks at the level of SWB. Our results imply
that online social networks may be equally subject to the social mechanisms
that cause assortative mixing in real social networks and that such assortative
mixing takes place at the level of SWB. Given the increasing prevalence of
online social networks, their propensity to connect users with similar levels
of SWB may be an important instrument in better understanding how both positive
and negative sentiments spread through online social ties. Future research may
focus on how event-specific mood states can propagate and influence user
behavior in "real life".Comment: 17 pages, 9 figure
Disassortative mixing in online social networks
The conventional wisdom is that social networks exhibit an assortative mixing
pattern, whereas biological and technological networks show a disassortative
mixing pattern. However, the recent research on the online social networks
modifies the widespread belief, and many online social networks show a
disassortative or neutral mixing feature. Especially, we found that an online
social network, Wealink, underwent a transition from degree assortativity
characteristic of real social networks to degree disassortativity
characteristic of many online social networks, and the transition can be
reasonably elucidated by a simple network model that we propose. The relations
among network assortativity, clustering, and modularity are also discussed in
the paper.Comment: 6 pages, 5 figures, 1 tabl
Twitter reciprocal reply networks exhibit assortativity with respect to happiness
The advent of social media has provided an extraordinary, if imperfect, 'big
data' window into the form and evolution of social networks. Based on nearly 40
million message pairs posted to Twitter between September 2008 and February
2009, we construct and examine the revealed social network structure and
dynamics over the time scales of days, weeks, and months. At the level of user
behavior, we employ our recently developed hedonometric analysis methods to
investigate patterns of sentiment expression. We find users' average happiness
scores to be positively and significantly correlated with those of users one,
two, and three links away. We strengthen our analysis by proposing and using a
null model to test the effect of network topology on the assortativity of
happiness. We also find evidence that more well connected users write happier
status updates, with a transition occurring around Dunbar's number. More
generally, our work provides evidence of a social sub-network structure within
Twitter and raises several methodological points of interest with regard to
social network reconstructions.Comment: 22 pages, 21 figures, 5 tables, In press at the Journal of
Computational Scienc
ELASTICITY: Topological Characterization of Robustness in Complex Networks
Just as a herd of animals relies on its robust social structure to survive in
the wild, similarly robustness is a crucial characteristic for the survival of
a complex network under attack. The capacity to measure robustness in complex
networks defines the resolve of a network to maintain functionality in the
advent of classical component failures and at the onset of cryptic malicious
attacks. To date, robustness metrics are deficient and unfortunately the
following dilemmas exist: accurate models necessitate complex analysis while
conversely, simple models lack applicability to our definition of robustness.
In this paper, we define robustness and present a novel metric, elasticity- a
bridge between accuracy and complexity-a link in the chain of network
robustness. Additionally, we explore the performance of elasticity on Internet
topologies and online social networks, and articulate results
Assortativity and leadership emergence from anti-preferential attachment in heterogeneous networks
Many real-world networks exhibit degree-assortativity, with nodes of similar
degree more likely to link to one another. Particularly in social networks, the
contribution to the total assortativity varies with degree, featuring a
distinctive peak slightly past the average degree. The way traditional models
imprint assortativity on top of pre-defined topologies is via degree-preserving
link permutations, which however destroy the particular graph's hierarchical
traits of clustering. Here, we propose the first generative model which creates
heterogeneous networks with scale-free-like properties and tunable realistic
assortativity. In our approach, two distinct populations of nodes are added to
an initial network seed: one (the followers) that abides by usual preferential
rules, and one (the potential leaders) connecting via anti-preferential
attachments, i.e. selecting lower degree nodes for their initial links. The
latter nodes come to develop a higher average degree, and convert eventually
into the final hubs. Examining the evolution of links in Facebook, we present
empirical validation for the connection between the initial anti-preferential
attachment and long term high degree. Thus, our work sheds new light on the
structure and evolution of social networks
Network Weirdness: Exploring the Origins of Network Paradoxes
Social networks have many counter-intuitive properties, including the
"friendship paradox" that states, on average, your friends have more friends
than you do. Recently, a variety of other paradoxes were demonstrated in online
social networks. This paper explores the origins of these network paradoxes.
Specifically, we ask whether they arise from mathematical properties of the
networks or whether they have a behavioral origin. We show that sampling from
heavy-tailed distributions always gives rise to a paradox in the mean, but not
the median. We propose a strong form of network paradoxes, based on utilizing
the median, and validate it empirically using data from two online social
networks. Specifically, we show that for any user the majority of user's
friends and followers have more friends, followers, etc. than the user, and
that this cannot be explained by statistical properties of sampling. Next, we
explore the behavioral origins of the paradoxes by using the shuffle test to
remove correlations between node degrees and attributes. We find that paradoxes
for the mean persist in the shuffled network, but not for the median. We
demonstrate that strong paradoxes arise due to the assortativity of user
attributes, including degree, and correlation between degree and attribute.Comment: Accepted to ICWSM 201
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
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