92,384 research outputs found
A cross-model study on the effect of power-laws on language evolution
Based on three evolutionary computational models that respectively simulate lexical, categorical and syntactic evolutions, we explore the effect of power-law distributed social popularity on language origin and change. Simulation results reveal a critical scaling degree (λ ≈ 1.0) in power-law distributions that helps accelerate the diffusion of linguistic conventions and preserve high linguistic understandability in population. Other scaling degrees (λ = 0.0 or λ > 1.0), however, tend to delay such diffusion process and affect linguistic understandability. Apart from the conventionalization nature of language communications in these models, increase in population size could also contribute to select the critical scaling degree, since this scaling degree can accommodate the influence of population size on linguistic understandability and many power-laws in real-world systems have their scaling degrees around this critical value.published_or_final_versio
Impact of local information in growing networks
We present a new model of the evolutionary dynamics and the growth of on-line
social networks. The model emulates people's strategies for acquiring
information in social networks, emphasising the local subjective view of an
individual and what kind of information the individual can acquire when
arriving in a new social context. The model proceeds through two phases: (a) a
discovery phase, in which the individual becomes aware of the surrounding world
and (b) an elaboration phase, in which the individual elaborates locally the
information trough a cognitive-inspired algorithm. Model generated networks
reproduce main features of both theoretical and real-world networks, such as
high clustering coefficient, low characteristic path length, strong division in
communities, and variability of degree distributions.Comment: In Proceedings Wivace 2013, arXiv:1309.712
Evolution of Ego-networks in Social Media with Link Recommendations
Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl
A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)
In the 1990s, Agent-Based Modeling (ABM) began gaining popularity and represents a departure from the more classical simulation approaches. This departure, its recent development and its increasing application by non-traditional simulation disciplines indicates the need to continuously assess the current state of ABM and identify opportunities for improvement. To begin to satisfy this need, we surveyed and collected data from 279 articles from 92 unique publication outlets in which the authors had constructed and analyzed an agent-based model. From this large data set we establish the current practice of ABM in terms of year of publication, field of study, simulation software used, purpose of the simulation, acceptable validation criteria, validation techniques and complete description of the simulation. Based on the current practice we discuss six improvements needed to advance ABM as an analysis tool. These improvements include the development of ABM specific tools that are independent of software, the development of ABM as an independent discipline with a common language that extends across domains, the establishment of expectations for ABM that match their intended purposes, the requirement of complete descriptions of the simulation so others can independently replicate the results, the requirement that all models be completely validated and the development and application of statistical and non-statistical validation techniques specifically for ABM.Agent-Based Modeling, Survey, Current Practices, Simulation Validation, Simulation Purpose
Online Popularity and Topical Interests through the Lens of Instagram
Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201
Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data
Use of socially generated "big data" to access information about collective
states of the minds in human societies has become a new paradigm in the
emerging field of computational social science. A natural application of this
would be the prediction of the society's reaction to a new product in the sense
of popularity and adoption rate. However, bridging the gap between "real time
monitoring" and "early predicting" remains a big challenge. Here we report on
an endeavor to build a minimalistic predictive model for the financial success
of movies based on collective activity data of online users. We show that the
popularity of a movie can be predicted much before its release by measuring and
analyzing the activity level of editors and viewers of the corresponding entry
to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the
dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi
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