1,944 research outputs found
The Directed Closure Process in Hybrid Social-Information Networks, with an Analysis of Link Formation on Twitter
It has often been taken as a working assumption that directed links in
information networks are frequently formed by "short-cutting" a two-step path
between the source and the destination -- a kind of implicit "link copying"
analogous to the process of triadic closure in social networks. Despite the
role of this assumption in theoretical models such as preferential attachment,
it has received very little direct empirical investigation. Here we develop a
formalization and methodology for studying this type of directed closure
process, and we provide evidence for its important role in the formation of
links on Twitter. We then analyze a sequence of models designed to capture the
structural phenomena related to directed closure that we observe in the Twitter
data
Analysis of a continuous-time model of structural balance
It is not uncommon for certain social networks to divide into two opposing
camps in response to stress. This happens, for example, in networks of
political parties during winner-takes-all elections, in networks of companies
competing to establish technical standards, and in networks of nations faced
with mounting threats of war. A simple model for these two-sided separations is
the dynamical system dX/dt = X^2 where X is a matrix of the friendliness or
unfriendliness between pairs of nodes in the network. Previous simulations
suggested that only two types of behavior were possible for this system: either
all relationships become friendly, or two hostile factions emerge. Here we
prove that for generic initial conditions, these are indeed the only possible
outcomes. Our analysis yields a closed-form expression for faction membership
as a function of the initial conditions, and implies that the initial amount of
friendliness in large social networks (started from random initial conditions)
determines whether they will end up in intractable conflict or global harmony.Comment: 12 pages, 2 figure
Modeling self-organization of communication and topology in social networks
This paper introduces a model of self-organization between communication and
topology in social networks, with a feedback between different communication
habits and the topology. To study this feedback, we let agents communicate to
build a perception of a network and use this information to create strategic
links. We observe a narrow distribution of links when the communication is low
and a system with a broad distribution of links when the communication is high.
We also analyze the outcome of chatting, cheating, and lying, as strategies to
get better access to information in the network. Chatting, although only
adopted by a few agents, gives a global gain in the system. Contrary, a global
loss is inevitable in a system with too many liarsComment: 6 pages 7 figures, Java simulation available at
http://cmol.nbi.dk/models/inforew/inforew.htm
Coordination and Efficiency in Decentralized Collaboration
Environments for decentralized on-line collaboration are now widespread on
the Web, underpinning open-source efforts, knowledge creation sites including
Wikipedia, and other experiments in joint production. When a distributed group
works together in such a setting, the mechanisms they use for coordination can
play an important role in the effectiveness of the group's performance.
Here we consider the trade-offs inherent in coordination in these on-line
settings, balancing the benefits to collaboration with the cost in effort that
could be spent in other ways. We consider two diverse domains that each contain
a wide range of collaborations taking place simultaneously -- Wikipedia and
GitHub -- allowing us to study how coordination varies across different
projects. We analyze trade-offs in coordination along two main dimensions,
finding similar effects in both our domains of study: first we show that, in
aggregate, high-status projects on these sites manage the coordination
trade-off at a different level than typical projects; and second, we show that
projects use a different balance of coordination when they are "crowded," with
relatively small size but many participants. We also develop a stylized
theoretical model for the cost-benefit trade-off inherent in coordination and
show that it qualitatively matches the trade-offs we observe between
crowdedness and coordination.Comment: 10 pages, 6 figures, ICWSM 2015, in Proc. 9th International AAAI
Conference on Weblogs and Social Medi
Spectral centrality measures in complex networks
Complex networks are characterized by heterogeneous distributions of the
degree of nodes, which produce a large diversification of the roles of the
nodes within the network. Several centrality measures have been introduced to
rank nodes based on their topological importance within a graph. Here we review
and compare centrality measures based on spectral properties of graph matrices.
We shall focus on PageRank, eigenvector centrality and the hub/authority scores
of HITS. We derive simple relations between the measures and the (in)degree of
the nodes, in some limits. We also compare the rankings obtained with different
centrality measures.Comment: 11 pages, 10 figures, 5 tables. Final version published in Physical
Review
Superlinear Scaling for Innovation in Cities
Superlinear scaling in cities, which appears in sociological quantities such
as economic productivity and creative output relative to urban population size,
has been observed but not been given a satisfactory theoretical explanation.
Here we provide a network model for the superlinear relationship between
population size and innovation found in cities, with a reasonable range for the
exponent.Comment: 5 pages, 5 figures, 1 table, submitted to Phys. Rev. E; references
corrected; figures corrected, references and brief discussion adde
Web-based text anonymization with Node.js: Introducing NETANOS (Named entity-based Text Anonymization for Open Science)
Paradoxes in Fair Computer-Aided Decision Making
Computer-aided decision making--where a human decision-maker is aided by a
computational classifier in making a decision--is becoming increasingly
prevalent. For instance, judges in at least nine states make use of algorithmic
tools meant to determine "recidivism risk scores" for criminal defendants in
sentencing, parole, or bail decisions. A subject of much recent debate is
whether such algorithmic tools are "fair" in the sense that they do not
discriminate against certain groups (e.g., races) of people.
Our main result shows that for "non-trivial" computer-aided decision making,
either the classifier must be discriminatory, or a rational decision-maker
using the output of the classifier is forced to be discriminatory. We further
provide a complete characterization of situations where fair computer-aided
decision making is possible
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