3,147 research outputs found
A nonuniform popularity-similarity optimization (nPSO) model to efficiently generate realistic complex networks with communities
The hidden metric space behind complex network topologies is a fervid topic
in current network science and the hyperbolic space is one of the most studied,
because it seems associated to the structural organization of many real complex
systems. The Popularity-Similarity-Optimization (PSO) model simulates how
random geometric graphs grow in the hyperbolic space, reproducing strong
clustering and scale-free degree distribution, however it misses to reproduce
an important feature of real complex networks, which is the community
organization. The Geometrical-Preferential-Attachment (GPA) model was recently
developed to confer to the PSO also a community structure, which is obtained by
forcing different angular regions of the hyperbolic disk to have variable level
of attractiveness. However, the number and size of the communities cannot be
explicitly controlled in the GPA, which is a clear limitation for real
applications. Here, we introduce the nonuniform PSO (nPSO) model that,
differently from GPA, forces heterogeneous angular node attractiveness by
sampling the angular coordinates from a tailored nonuniform probability
distribution, for instance a mixture of Gaussians. The nPSO differs from GPA in
other three aspects: it allows to explicitly fix the number and size of
communities; it allows to tune their mixing property through the network
temperature; it is efficient to generate networks with high clustering. After
several tests we propose the nPSO as a valid and efficient model to generate
networks with communities in the hyperbolic space, which can be adopted as a
realistic benchmark for different tasks such as community detection and link
prediction
From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices
This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device userās daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)
Contagion aĢ effet de seuil dans les reĢseaux complexes
Networks arise frequently in the study of complex systems, since interactions among the components of such systems are critical. Networks can act as a substrate for dynamical process, such as the diffusion of information or disease throughout populations. Network structure can determine the temporal evolution of a dynamical process, including the characteristics of the steady state.The simplest representation of a complex system is an undirected, unweighted, single layer graph. In contrast, real systems exhibit heterogeneity of interaction strength and type. Such systems are frequently represented as weighted multiplex networks, and in this work we incorporate these heterogeneities into a master equation formalism in order to study their effects on spreading processes. We also carry out simulations on synthetic and empirical networks, and show that spreading dynamics, in particular the speed at which contagion spreads via threshold mechanisms, depend non-trivially on these heterogeneities. Further, we show that an important family of networks undergo reentrant phase transitions in the size and frequency of global cascades as a result of these interactions.A challenging feature of real systems is their tendency to evolve over time, since the changing structure of the underlying network is critical to the behaviour of overlying dynamical processes. We show that one aspect of temporality, the observed āburstinessā in interaction patterns, leads to non-monotic changes in the spreading time of threshold driven contagion processes.The above results shed light on the effects of various network heterogeneities, with respect to dynamical processes that evolve on these networks.Les interactions entre les composants des systeĢmes complexes font eĢmerger diffeĢrents types de reĢseaux. Ces reĢseaux peuvent jouer le roĢle dāun substrat pour des processus dynamiques tels que la diffusion dāinformations ou de maladies dans des populations. Les structures de ces reĢseaux deĢterminent lāeĢvolution dāun processus dynamique, en particulier son reĢgime transitoire, mais aussi les caracteĢristiques du reĢgime permanent.Les systeĢmes complexes reĢels manifestent des inteĢractions heĢteĢrogeĢnes en type et en intensiteĢ. Ces systeĢmes sont repreĢseteĢs comme des reĢseaux pondeĢreĢs aĢ plusieurs couches. Dans cette theĢse, nous deĢveloppons une eĢquation maiĢtresse afin dāinteĢgrer ces heĢteĢrogeĢneĢiteĢs et dāeĢtudier leurs effets sur les processus de diffusion. AĢ lāaide de simulations mettant en jeu des reĢseaux reĢels et geĢneĢreĢs, nous montrons que les dynamiques de diffusion sont lieĢes de manieĢre non triviale aĢ lāheĢteĢrogeĢneĢiteĢ de ces reĢseaux, en particulier la vitesse de propagation dāune contagion baseĢe sur un effet de seuil. De plus, nous montrons que certaines classes de reĢseaux sont soumises aĢ des transitions de phase reĢentrantes fonctions de la taille des āglobal cascadesā.La tendance des reĢseaux reĢels aĢ eĢvoluer dans le temps rend difficile la modeĢlisation des processus de diffusion. Nous montrons enfin que la dureĢe de diffusion dāun processus de contagion baseĢ sur un effet de seuil change de manieĢre non-monotone du fait de la preĢsence deārafalesā dans les motifs dāinteĢractions. Lāensemble de ces reĢsultats mettent en lumieĢre les effets de lāheĢteĢrogeĢneĢiteĢ des reĢseaux vis-aĢ-vis des processus dynamiques y eĢvoluant
Friendships between women: a critical review
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No Man is an Island: the Inter-personal Determinants of Regional Well-Being in Europe
There is a strong need to complement the analysis of social well-being at the European
regional level to supplement existing, predominantly economic analysis. This
work extends the measurement of well-being across the EU-15 regions in several ways.
First, we assess the determinants of well-being using a multilevel modelling approach using data at the national, regional and individual levels. Second, we have extended the model to account for the effects of social interactions within each group, as well as intrinsic socio-demographic indicators and higher-level exogenous contextual factors. Empirical findings support the idea that well-being is strongly dependent both on these general forms of social interactions and on more specific individual characteristics. We find that there is some evidence of greater regional effects relative to national effects,
but individual well-being continues to be affected most by micro-level phenomena
Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network
Topological evolution over time in a dynamic network triggers both the addition and deletion of actors and the links among them. A dynamic network can be represented as a time series of network snapshots where each snapshot represents the state of the network over an interval of time (for example, a minute, hour or day). The duration of each snapshot denotes the temporal scale/sliding window of the dynamic network and all the links within the duration of the window are aggregated together irrespective of their order in time. The inherent trade-off in selecting the timescale in analysing dynamic networks is that choosing a short temporal window may lead to chaotic changes in network topology and measures (for example, the actorsā centrality measures and the average path length); however, choosing a long window may compromise the study and the investigation of network dynamics. Therefore, to facilitate the analysis and understand different patterns of actor-oriented evolutionary aspects, it is necessary to define an optimal window length (temporal duration) with which to sample a dynamic network. In addition to determining the optical temporal duration, another key task for understanding the dynamics of evolving networks is being able to predict the likelihood of future links among pairs of actors given the existing states of link structure at present time. This phenomenon is known as the link prediction problem in network science. Instead of considering a static state of a network where the associated topology does not change, dynamic link prediction attempts to predict emerging links by considering different types of historical/temporal information, for example the different types of temporal evolutions experienced by the actors in a dynamic network due to the topological evolution over time, known as actor dynamicities. Although there has been some success in developing various methodologies and metrics for the purpose of dynamic link prediction, mining actor-oriented evolutions to address this problem has received little attention from the research community. In addition to this, the existing methodologies were developed without considering the sampling window size of the dynamic network, even though the sampling duration has a large impact on mining the network dynamics of an evolutionary network. Therefore, although the principal focus of this thesis is link prediction in dynamic networks, the optimal sampling window determination was also considered
Unequal Networks:
Does the neighbourhood in which people live matter for the resourcefulness of their personal network and thus for their opportunities in life? Do residents of a multi-ethnic āproblemā area maintain fewer relationships with fellow residents compared to residents of a homogeneous problem-free neighbourhood? And do ādiversity-seekersā who choose to live in a mixed neighbourhood translate their liking for diversity into more mixed networks and more bridging ties? This book brings together key insights from urban studies and network studies in order to understand whether and how spatial segregation matters for personal networks and inequality. By approaching these questions through different urban sociological perspectives, the book engages with current debates on poverty concentration as well as ethnic diversity, gentrification and social capital. The study is based on detailed quantitative and qualitative data on the personal networks of people living in three differently composed neighbourhoods in Rotterdam, the second largest city in the Netherlands
Dynamics of Quality Perception in a Social Network: A Cellular Automaton Based Model in Aesthetics Services
AbstractAn attempt was carried out to simulate interactions between customers and providers, and understand the rationality of a social network using a cellular automata model. A longitudinal research study was conducted, based on a dyadic perspective in aesthetics clinics, approaching clients and service providers. The evolution of opinions regarding the associated service quality was then modeled with a cellular automaton. Based on an existing and valid scale of service quality, six semi-structured interviews with clients and service providers were carried out. The indicators were then refined and two quantitative surveys were performed, with a time interval of four months. A cellular automaton rule was then searched for that could simulate the network rationality between the two surveys. The proposed cellular automaton model achieved an accuracy of 73.80%, a higher value than the ones typically found in linear regression models of the service quality literature. The simulation allowed to understand which behaviours adopted by providers and customers generate an improved perception of service quality. The simulation also identified dissatisfied individuals in the social network and the way they influence the network. These findings may help managers to control employees' conducts and the service performance
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