388,981 research outputs found
Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events
Ample theoretical work on social networks is explicitly or implicitly concerned with the role of interpersonal interaction. However, empirical studies to date mostly focus on the analysis of stable relations. This article introduces Dynamic Network Actor Models (DyNAMs) for the study of directed, interpersonal interaction through time. The presented model addresses three important aspects of interpersonal interaction. First, interactions unfold in a larger social context and depend on complex structures in social systems. Second, interactions emanate from individuals and are based on personal preferences, restricted by the available interaction opportunities. Third, sequences of interactions develop dynamically, and the timing of interactions relative to one another contains useful information. We refer to these aspects as the network nature, the actor-oriented nature, and the dynamic nature of social interaction. A case study compares the DyNAM framework to the relational event model, a widely used statistical method for the study of social interaction data
A human factors approach to analysing military command and control
This paper applies the Event Analysis for Systemic Teamwork (EAST) method to an example of military command and control. EAST offers a way to describe system level 'emergent properties' that arise from the complex interactions of system components (human and technical). These are described using an integrated methods approach and modelled using Task, Social and Knowledge networks. The current article is divided into three parts: a brief description of the military command and control context, a brief description of the EAST method, and a more in depth presentation of the analysis outcomes. Numerous findings emerge from the application of the method. These findings are compared with similar analyses undertaken in civilian domains, where Network Enabled Capability (NEC) is already in place. The emergent properties of the military scenario relate to the degree of system reconfigurability, systems level Situational Awareness (SA), team-working and the role of mediating technology. It is argued that the EAST method can be used to offer several interesting perspectives on designing and specifying NEC capability in military context
Analysis of properties of complex networks with discrete dynamics
Комплексне мреже су се у току последње две деценије показале као изузетно користан концепт у проучавању карактеристика комплексних система...In the last two decades, complex networks have been proven as very useful
concept for examination of properties of complex systems. The first step within
this framework is to extract individual elements of the system and to represent
interactions between these elements in the form of complex network. After this step,
the study of complex system organization is reduced to the analysis of structure and
dynamical processes on network with the use of suitable methodology.
The increase in a variety of real systems with available data, which enable insight
into the structure of network of interactions, requires constant development of new
techniques and theoretical models that could explain behavior of specific systems.
In this thesis, we studied complex networks with discrete dynamics using data on
event-based social systems. There has been very little previous research on properties
of networks representing these systems. One of the main reasons is availability of
data. In the given systems individuals interact face-to-face, wherefore it is more
difficult to get the data, than in the case of social systems where individuals use
some communication device to communicate with each other.
Special attention was paid to examination of activity of individuals in group
events. According to the results of statistical analysis of empirical data it has been
shown that individuals do not attend events randomly. We analysed mathematical
models that can explain member’s participation patterns on events which turned
out to be strongly heterogeneous. It has been shown that generalized binary P´olya
model can reproduce given empirical results successfully.
Using bipartite networks ensemble with maximum entropy, we identified significant
connections in weighted network that represent relevant social interactions. In order
to get the insight into evolution of the network structure, we analyzed change of
local structural parameters after each event attendance. It has been shown that
members of the system establish new connections with neighbors during member’s
early involvement in the group activities, while later, as number of attended events
increase, the interactions with neighbors and strenghtening of existing communities
become preferred in comparison to forming new connections in network.
In order to analyse the influence that particular event has on network structure,
we proposed an approach based on event removal according to different criteria and
examination of resulting structural changes in network. The results showed that
interactions between individuals with strong connections are dominant on events
with small number of members, with small number of members, while during the large events typically individuals
with weak connections that could be easily broken interact
Analyzing Users' Activity in On-line Social Networks over Time through a Multi-Agent Framework
[EN] The number of people and organizations using online social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an online event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. In this article, we present a multi-agent system that automates the process of gathering data from users activity in social networks and performs an in-depth analysis of the evolution of social behavior at different levels of granularity in online events based on network theory metrics. We evaluated its functionality analyzing users activity in events on Twitter.This work is partially supported by the PROME-TEOII/2013/019, TIN2014-55206-R, TIN2015-65515-C4-1-R, H2020-ICT-2015-688095.Del Val Noguera, E.; Martínez, C.; Botti, V. (2016). Analyzing Users' Activity in On-line Social Networks over Time through a Multi-Agent Framework. Soft Computing. 20(11):4331-4345. https://doi.org/10.1007/s00500-016-2301-0S433143452011Ahn Y-Y, Han S, Kwak H, Moon S, Jeong H (2007) Analysis of topological characteristics of huge online social networking services. 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Springer, Berlin, pp 191–201del Val E, Rebollo M, Botti V (2015b) Does the type of event influence how user interactions evolve on twitter? PLOS One 10(5):e0124049Eurostat (2016a) Internet use statistics—individuals. http://ec.europa.eu/eurostat/statistics-explained/index.php/Internet_use_statistics_-_individuals . Accessed 29 April 2016Eurostat (2016b) Social media—statistics on the use by enterprises. http://ec.europa.eu/eurostat/statistics-explained/index.php/Social_media_-_statistics_on_the_use_by_enterprises#Further_Eurostat_information . Accessed 29 April 2016García Fornes AM, Rodrigo Solaz M, Terrasa Barrena AM, Inglada J, Javier V, Jorge Cano J, Mulet Mengual L, Palomares Chust A, Búrdalo Rapa LA, Giret Boggino AS et al (2015) Magentix 2 user’s manualGolbeck J, Robles C, Turner K (2011) Predicting personality with social media. In: CHI’11, pp 253–262Guimerà R, Llorente A, Moro E, Sales-Pardo M (2012) Predicting human preferences using the block structure of complex social networks. PloS One 7(9):e44620Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. arXiv preprint arXiv:0812.1045Jamali M, Abolhassani H (2006) Different aspects of social network analysis. In: 2006 IEEE/WIC/ACM international conference on web intelligence (WI 2006 main conference proceedings)(WI’06). IEEE, pp 66–72Jiang Y, Jiang J (2014) Understanding social networks from a multiagent perspective. Parallel Distrib Syst IEEE Trans 25(10):2743–2759Kossinets G, Watts D (2006) Empirical analysis of an evolving social network. Science 311(5757):88–90Kumar R, Novak J, Tomkins A (2010) Structure and evolution of online social networks. In: Yu PS, Han J, Faloutsos C (eds) Link mining: models, algorithms, and applications. 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Beyond 'Global Production Networks': Australian Fashion Week's Trans-Sectoral Synergies
When studies of industrial organisation are informed by commodity chain, actor network, or global production network theories and focus on tracing commodity flows, social networks, or a combination of the two, they can easily overlook the less routine trans-sectoral
associations that are crucial to the creation and realisation of value. This paper shifts attention to
identifying the sites at which diverse specialisations meet to concentrate and amplify mutually reinforcing circuits of value. These valorisation processes are demonstrated in the case of Australian Fashion Week, an event in which multiple interests converge to synchronize different expressions
of fashion ideas, actively construct fashion markets and enhance the value of a diverse range of fashionable commodities. Conceptualising these interconnected industries as components of a trans-sectoral fashion complex has implications for understanding regional development, world cities, production location, and the manner in which production systems “touch down” in different
places
Characterizing interactions in online social networks during exceptional events
Nowadays, millions of people interact on a daily basis on online social media
like Facebook and Twitter, where they share and discuss information about a
wide variety of topics. In this paper, we focus on a specific online social
network, Twitter, and we analyze multiple datasets each one consisting of
individuals' online activity before, during and after an exceptional event in
terms of volume of the communications registered. We consider important events
that occurred in different arenas that range from policy to culture or science.
For each dataset, the users' online activities are modeled by a multilayer
network in which each layer conveys a different kind of interaction,
specifically: retweeting, mentioning and replying. This representation allows
us to unveil that these distinct types of interaction produce networks with
different statistical properties, in particular concerning the degree
distribution and the clustering structure. These results suggests that models
of online activity cannot discard the information carried by this multilayer
representation of the system, and should account for the different processes
generated by the different kinds of interactions. Secondly, our analysis
unveils the presence of statistical regularities among the different events,
suggesting that the non-trivial topological patterns that we observe may
represent universal features of the social dynamics on online social networks
during exceptional events
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
Mapping London's Innovation Networks
A wide range of authors have highlighted the potential benefits for entrepreneurial companies that engage in effective networking along and across the supply-chain. As many organisations have downsized or outsourcedbasic research activities Universities have an increasingly important role within such networks. A number of UK initiatives have been established to encourage greater 'entanglement' between academia and commerce; the London Technology Network is one example which is intended to encourage interactions between London's leading research institutes and innovative businesses.Using the detailed data acquired by this network this paper is intended to presents an exploratory analysis of such activities with the aim of establishing the extent to which company size, sector and/or location play a significant role in participation in the network's activities. A wide range of authors have highlighted the potential benefits for entrepreneurial companies that engage in effective networking along and across the supply-chain. As many organisations have downsized or outsourced basic research activities Universities have an increasingly important role within such networks. A number of UK initiatives have been established to encourage greater 'entanglement' between academia and commerce; the London Technology Network is one example which is intended to encourage interactions between London's leading research institutes and innovative businesses.Using the detailed data acquired by this network this paper is intended to presents an exploratory analysis of such activities with the aim of establishing the extent to which company size, sector and/or location play a significant role in participation in the network's activities
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
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