99 research outputs found
Building the Brazilian Academic Genealogy Tree
Along the history, many researchers provided remarkable contributions to
science, not only advancing knowledge but also in terms of mentoring new
scientists. Currently, identifying and studying the formation of researchers
over the years is a challenging task as current repositories of theses and
dissertations are cataloged in a decentralized way through many local digital
libraries. Following our previous work in which we created and analyzed a large
collection of genealogy trees extracted from NDLTD, in this paper we focus our
attention on building such trees for the Brazilian research community. For
this, we use data from the Lattes Platform, an internationally renowned
initiative from CNPq, the Brazilian National Council for Scientific and
Technological Development, for managing information about individual
researchers and research groups in Brazil
Timing interactions in social simulations: The voter model
The recent availability of huge high resolution datasets on human activities
has revealed the heavy-tailed nature of the interevent time distributions. In
social simulations of interacting agents the standard approach has been to use
Poisson processes to update the state of the agents, which gives rise to very
homogeneous activity patterns with a well defined characteristic interevent
time. As a paradigmatic opinion model we investigate the voter model and review
the standard update rules and propose two new update rules which are able to
account for heterogeneous activity patterns. For the new update rules each node
gets updated with a probability that depends on the time since the last event
of the node, where an event can be an update attempt (exogenous update) or a
change of state (endogenous update). We find that both update rules can give
rise to power law interevent time distributions, although the endogenous one
more robustly. Apart from that for the exogenous update rule and the standard
update rules the voter model does not reach consensus in the infinite size
limit, while for the endogenous update there exist a coarsening process that
drives the system toward consensus configurations.Comment: Book Chapter, 23 pages, 9 figures, 5 table
How citation boosts promote scientific paradigm shifts and Nobel Prizes
Nobel Prizes are commonly seen to be among the most prestigious achievements
of our times. Based on mining several million citations, we quantitatively
analyze the processes driving paradigm shifts in science. We find that
groundbreaking discoveries of Nobel Prize Laureates and other famous scientists
are not only acknowledged by many citations of their landmark papers.
Surprisingly, they also boost the citation rates of their previous
publications. Given that innovations must outcompete the rich-gets-richer
effect for scientific citations, it turns out that they can make their way only
through citation cascades. A quantitative analysis reveals how and why they
happen. Science appears to behave like a self-organized critical system, in
which citation cascades of all sizes occur, from continuous scientific progress
all the way up to scientific revolutions, which change the way we see our
world. Measuring the "boosting effect" of landmark papers, our analysis reveals
how new ideas and new players can make their way and finally triumph in a world
dominated by established paradigms. The underlying "boost factor" is also
useful to discover scientific breakthroughs and talents much earlier than
through classical citation analysis, which by now has become a widespread
method to measure scientific excellence, influencing scientific careers and the
distribution of research funds. Our findings reveal patterns of collective
social behavior, which are also interesting from an attention economics
perspective. Understanding the origin of scientific authority may therefore
ultimately help to explain, how social influence comes about and why the value
of goods depends so strongly on the attention they attract.Comment: 6 pages, 6 figure
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
Correlated dynamics in egocentric communication networks
We investigate the communication sequences of millions of people through two
different channels and analyze the fine grained temporal structure of
correlated event trains induced by single individuals. By focusing on
correlations between the heterogeneous dynamics and the topology of egocentric
networks we find that the bursty trains usually evolve for pairs of individuals
rather than for the ego and his/her several neighbors thus burstiness is a
property of the links rather than of the nodes. We compare the directional
balance of calls and short messages within bursty trains to the average on the
actual link and show that for the trains of voice calls the imbalance is
significantly enhanced, while for short messages the balance within the trains
increases. These effects can be partly traced back to the technological
constrains (for short messages) and partly to the human behavioral features
(voice calls). We define a model that is able to reproduce the empirical
results and may help us to understand better the mechanisms driving technology
mediated human communication dynamics.Comment: 7 pages, 6 figure
Emotional persistence in online chatting communities
How do users behave in online chatrooms, where they instantaneously read and
write posts? We analyzed about 2.5 million posts covering various topics in
Internet relay channels, and found that user activity patterns follow known
power-law and stretched exponential distributions, indicating that online chat
activity is not different from other forms of communication. Analysing the
emotional expressions (positive, negative, neutral) of users, we revealed a
remarkable persistence both for individual users and channels. I.e. despite
their anonymity, users tend to follow social norms in repeated interactions in
online chats, which results in a specific emotional "tone" of the channels. We
provide an agent-based model of emotional interaction, which recovers
qualitatively both the activity patterns in chatrooms and the emotional
persistence of users and channels. While our assumptions about agent's
emotional expressions are rooted in psychology, the model allows to test
different hypothesis regarding their emotional impact in online communication.Comment: 34 pages, 4 main and 12 supplementary figure
Who is the best player ever? A complex network analysis of the history of professional tennis
We consider all matches played by professional tennis players between 1968
and 2010, and, on the basis of this data set, construct a directed and weighted
network of contacts. The resulting graph shows complex features, typical of
many real networked systems studied in literature. We develop a diffusion
algorithm and apply it to the tennis contact network in order to rank
professional players. Jimmy Connors is identified as the best player of the
history of tennis according to our ranking procedure. We perform a complete
analysis by determining the best players on specific playing surfaces as well
as the best ones in each of the years covered by the data set. The results of
our technique are compared to those of two other well established methods. In
general, we observe that our ranking method performs better: it has a higher
predictive power and does not require the arbitrary introduction of external
criteria for the correct assessment of the quality of players. The present work
provides a novel evidence of the utility of tools and methods of network theory
in real applications.Comment: 10 pages, 4 figures, 4 table
A Human Development Framework for CO2 Reductions
Although developing countries are called to participate in CO2 emission
reduction efforts to avoid dangerous climate change, the implications of
proposed reduction schemes in human development standards of developing
countries remain a matter of debate. We show the existence of a positive and
time-dependent correlation between the Human Development Index (HDI) and per
capita CO2 emissions from fossil fuel combustion. Employing this empirical
relation, extrapolating the HDI, and using three population scenarios, the
cumulative CO2 emissions necessary for developing countries to achieve
particular HDI thresholds are assessed following a Development As Usual
approach (DAU). If current demographic and development trends are maintained,
we estimate that by 2050 around 85% of the world's population will live in
countries with high HDI (above 0.8). In particular, 300Gt of cumulative CO2
emissions between 2000 and 2050 are estimated to be necessary for the
development of 104 developing countries in the year 2000. This value represents
between 20% to 30% of previously calculated CO2 budgets limiting global warming
to 2{\deg}C. These constraints and results are incorporated into a CO2
reduction framework involving four domains of climate action for individual
countries. The framework reserves a fair emission path for developing countries
to proceed with their development by indexing country-dependent reduction rates
proportional to the HDI in order to preserve the 2{\deg}C target after a
particular development threshold is reached. Under this approach, global
cumulative emissions by 2050 are estimated to range from 850 up to 1100Gt of
CO2. These values are within the uncertainty range of emissions to limit global
temperatures to 2{\deg}C.Comment: 14 pages, 7 figures, 1 tabl
Local variation of hashtag spike trains and popularity in Twitter
We draw a parallel between hashtag time series and neuron spike trains. In
each case, the process presents complex dynamic patterns including temporal
correlations, burstiness, and all other types of nonstationarity. We propose
the adoption of the so-called local variation in order to uncover salient
dynamics, while properly detrending for the time-dependent features of a
signal. The methodology is tested on both real and randomized hashtag spike
trains, and identifies that popular hashtags present regular and so less bursty
behavior, suggesting its potential use for predicting online popularity in
social media.Comment: 7 pages, 7 figure
Emergence of Bursts and Communities in Evolving Weighted Networks
Understanding the patterns of human dynamics and social interaction, and the
way they lead to the formation of an organized and functional society are
important issues especially for techno-social development. Addressing these
issues of social networks has recently become possible through large scale data
analysis of e.g. mobile phone call records, which has revealed the existence of
modular or community structure with many links between nodes of the same
community and relatively few links between nodes of different communities. The
weights of links, e.g. the number of calls between two users, and the network
topology are found correlated such that intra-community links are stronger
compared to the weak inter-community links. This is known as Granovetter's "The
strength of weak ties" hypothesis. In addition to this inhomogeneous community
structure, the temporal patterns of human dynamics turn out to be inhomogeneous
or bursty, characterized by the heavy tailed distribution of inter-event time
between two consecutive events. In this paper, we study how the community
structure and the bursty dynamics emerge together in an evolving weighted
network model. The principal mechanisms behind these patterns are social
interaction by cyclic closure, i.e. links to friends of friends and the focal
closure, i.e. links to individuals sharing similar attributes or interests, and
human dynamics by task handling process. These three mechanisms have been
implemented as a network model with local attachment, global attachment, and
priority-based queuing processes. By comprehensive numerical simulations we
show that the interplay of these mechanisms leads to the emergence of heavy
tailed inter-event time distribution and the evolution of Granovetter-type
community structure. Moreover, the numerical results are found to be in
qualitative agreement with empirical results from mobile phone call dataset.Comment: 9 pages, 6 figure
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