Although the context is quite different, the closest work to the construction of this section is Ser-Giacomi et al. (2015), which shows that the most probable paths in a Marko- vian model of a very complicated temporal network (viz., ocean water transport in the Mediterranean) suffice to describe the network’s key features. Other works have looked at higher-order models in discrete time as a way to finesse the challenges of continu- ous time modeling as discussed here (Lambiotte et al. 2019; Rosvall et al. 2014). Despite the many differences of detail, our own model likewise shows that the most probable paths/flows suffice for capturing the essential dynamics of directed contact networks. In particular, this includes flows that the model assesses as highly probable, but whose asso- ciated contact motifs occur infrequently (or perhaps just once in a given data set): in our experiments, such flows reliably capture anomalous and even malicious behavior (see, for example “Data reduction and anomaly detection” section).
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campus network and (iii) times are the times at which given devices were connected to given APs. Contacts in our empirical trace are predicated on device colocation. That is, two devices simultaneously present at a given location are connected by a timestamped link in the inferred contact network. Through inducement shuffling we destroy node’s time and location prefer- ences, i.e. we decorrelate the relationships between nodes, times and locations. This in turn leads to a different set of colocation events and thus a modified contact network is induced. For the original and each induced contact network we simulate diffusion of a quantity starting from a randomly infected node under the Susceptible-Infected (SI) infection model . To the best of the authors ’ knowledge, the present work is the first to take a null models approach to isolating spreading impediments and catalysts in colocation-driven contact networks. More importantly, we believe the inducement-shuffled null models to be the first to enable reasoning about the second-order causal relationship between the events on which the contact network is predicated and subsequent spreading potential. Though motivated in the context of a wireless mobile device contact trace, we believe the inducement-shuffled null models presented in this paper may find broader applications in colocation contact networks, some well outside com- puter networks.
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DOI: 10.4236/eng.2017.911055 918 Engineering , finite element method  and discrete element method . The finite ele- ment method based on continuum mechanics is hardly employed to simulate the process of landslide, and although the discrete element method can be used to study the macroscopic rules of landslide, such as movement velocity and slide distance, it can’t provide the bridging relationships between the contacting rela- tionships between sliding blocks and their evolution rules in mesoscale and ma- croscopic movement rules during the process of landslide. Therefore, studies on mesostructures and evolution of contact networks are necessary to be done to pro- vide a new method to analyze the macroscopic mechanism of landslide.
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Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV- infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network- based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from ‘‘less-’’ to ‘‘more- frequent’’ injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective.
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In this paper we reported the results obtained from a simple model in which individuals perform a random walk and start interactions based on a close proximity rule. The key ingredient is the social attractiveness of the individuals, which has the effect of slowing down the random walk performed by the agents and determines the duration of their interactions. By means of numerical simulations, we observed that the model reproduces the results obtained from the empirical analysis of the human contact networks provided by the SocioPatterns collab- oration . The match between the model and the em- pirical results is independent of the numerical and func- tional form of the diverse parameters defining the model. However, the attractiveness distribution η(a) used in the model definition deserves a more detailed discussion. Its functional form is hard to access empirically, and it is likely to be in its turn the combination of different ele- ments, such as prestige, status, role, etc. Moreover, even though in general attractiveness is a relational variable – the same individual exerting different interest on dif- ferent agents – we have assumed the simplest case of a uniform distribution for the attractiveness. For this rea- son it is important to stress some facts that support our decision, and to investigate the effect of the attractive- ness distribution on the model outcome.
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Datasets from which wildlife contact networks of epidemiological importance can be inferred are becoming increasingly common. A largely unexplored facet of these data is finding evidence of spatial constraints on who has contact with whom, despite theoretical epidemiologists having long realized spatial constraints can play a critical role in infectious disease dynamics. A graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely deter- mined by the spatial arrangement of its nodes. Statistical techniques are also used to fit a series of mechanistic models for contact rates between individuals to the binary edge data representing presence or absence of observed contact. These are the basis for a second measure that quantifies the extent to which contacts are being mediated by distance. We apply these methods to a set of 128 contact networks of field voles (Microtus agrestis) inferred from mark– recapture data collected over 7 years and from four sites. Large fluctuations in vole abundance allow us to demonstrate that the networks become increas- ingly similar to spatial proximity graphs as vole density increases. The average number of contacts, k k l , was (i) positively correlated with vole density across the range of observed densities and (ii) for two of the four sites a saturating function of density. The implications for pathogen persistence in wildlife may be that persistence is relatively unaffected by fluctuations in host density because at low density k k l is low but hosts move more freely, and at high density k k l is high but transmission is hampered by local build-up of infected or recovered animals.
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bounded intervals for agreeing parametrisation and initial conditions (Ethier and Kurtz, 1986; Andersson and Britton, 2000). Also, we can couple the Markovian standard SIS model to the same branching process that we coupled to the Markovian standard SIR model. The logic follows through in exactly the same way except that the time T (see section 1.4.2) now gives a lower bound on the actual time at which the correspondence breaks down. This is because an individual can be infected more than once in the SIS model, and it is only when an individual that is currently infected receives an extra infectious contact that the correspondence breaks. Therefore, we get the same thresh- old and probability of an epidemic/invasion as for the Markovian standard SIR model (again, valid for large populations). Note that the probability of an epidemic/invasion from a single initial infected in the Markovian standard SIS model is thus equal to I( ∞ ) in the endemic equilibrium of the deterministic SIS model (when above the threshold). It should be stressed that for the stochastic SIS model the eventual outcome is al- ways extinction of the infection (given enough time) since, given any present state, the probability of the all-susceptible (absorbing) state arising in any future time interval is positive.
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Models that assume a homogeneously mixing population have predictive value for many diseases  and have allowed researchers to study many characteristics of an epidemic, such as the existence of threshold values for the spread of an infection  and the asymptotic solution for the density of infected people [6,22]. Homogeneous mixing models imply that a susceptible person is equally likely to acquire infection from any infectious person in the population [6,7,23]. This assumption simplifies anal- ysis and is a good approximation for highly transmissible diseases, such as those that spread through aerosol drop- lets. However, epidemics of close contact infections occur within populations made up of individuals who mostly spend time with close associates and do not mix with other individuals in the population completely at ran- dom. Therefore, the homogeneous mixing assumption is not very realistic for diseases that are spread through close contact.
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Hence, face-to-face and proximity interactions have long been the focus of major attention in social sciences and epidemiology [5,4,7,18] and recently vari- ous research groups have developed sensing devices and approaches to automati- cally measure these interaction networks [16,10,39,32,1,30,47,49]. Reality Mining (RM) , a study conducted in 2004 by the MIT Media Lab, was the first one to collect data from mobile phones to track the dynamics of a community of 100 business school students over a nine-month period. Following this seminal project, the Social Evolution study [32,33] tracked the everyday life of a whole undergraduate dormitory for almost 8 months using mobile phones (i.e. call logs, location data, and proximity interactions). This study was specifically designed to model the adoption of political opinions, the spreading of epidemics, the ef- fect of social interactions on depression and stress, and the eating and physical exercise habits. More recently, in the Friends and Family study 130 graduate students and their partners, sharing the same dormitory, carried smartphones running a mobile sensing platform for 15 months . Additional data were also collected from Facebook, credit card statements, surveys including questions about personality traits, group affiliations, daily mood states and sleep quality, etc.
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(Montgomery 1991; Mortensen and Vishwanath 1994). Additionally, following the ‘social capital as trust’ approach, those who find jobs by using contacts may feel additional peer pressure to perform and thus attain higher productivity and wages (Kandel and Lazear 1992). However, while positive wage effects from using job contacts are sometimes found, this is far from universal and has led to consideration being given as to why using job contacts may appear to lower wages (Delattre and Sabatier 2007). One explanation centres on training costs (Pellizzari 2010). Firms may only be able to expend extra effort in using formal means of filling posts (for example, advertising or using recruitment agencies), rather than informal means (using social networks). Consequently, the use of formal means of filling posts will be more common where the costs of the posts remaining unfilled are high, as is likely to be the case with posts with high training costs (and consequent high wages). A second explanation centres on job seeker impatience: those keen to find employment quickly may use job contacts, sacrificing potentially higher wages from better matched posts for quicker entry into work (Bentolila, Michelacci, and Suarez 2010).
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Some papers have focused on characterizing within-school contact behav- ior in the context of understanding disease transmission. Glass and Glass (2008) administered contact surveys in an American elementary, middle and high school, and characterized contact duration and intensity by grade and location. Conlan et al. (2011) developed a new method to collect contact network data and analyzed mixing patterns, clustering and other network properties in 11 British primary schools. Although these studies provide important information regarding contact behavior within schools, neither develops a method for inference of the entire within-school contact network. Cauchemez et al. (2009) analyzed network and symptom status data in a fourth grade class during the H1N1 influenza pandemic. They found that selective mixing by gender influences the disease dynamics, but found no evidence for a playmate network or classroom neighbor effect on the trans- mission probability. However, because the sample size was small and asymp- tomatic and unobserved cases were not accounted for in the analysis, their findings are not definitive. Stehl´e et al. (2011a) describe a face-toface con- tact network in a primary school using proximity sensor data. Salath´e et al. (2010) analyze wireless sensor data to describe the contact network in an American high school and demonstrate through simulation studies that us- ing network data to inform interventions can reduce the disease burden. Xia et al. (2010) demonstrate that modeling network structure within schools in a large-scale simulation model can impact global epidemic dynamics.
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My study has refined the parameterization of two key epidemiological aspects in modeling disease spread in contact networks which have direct relevance for better understanding the transmission and ecology of wildlife diseases: estimating individual-based heterogeneities in mixing patterns and incorporating temporal dynamics of network structure. I use tuneable algorithms and formulas to test model assumptions about contact patterns and the impact of population structure on disease transmission through dynamic contact networks. This offers a computationally tractable framework to predict and model epidemic outbreaks in wildlife diseases. Most network models incorporate social contacts with the underlying assumption that mixing rates are static, which means that once an association has been formed between individuals or groups they will remain unaltered. Nonetheless, because of the great diversity in individual behaviour and social structure in natural populations this assumption is rarely a realistic approximation of the real contact patterns in social networks and usually fails to yield reliable estimates of disease dynamics (Fefferman and Ng 2007). Furthermore, network models usually generalize the dynamic processes of infection and maintain important global network metrics such as mean degree and clustering fixed in time. My study uses dynamic contacts networks by shifting associations between individuals according to key ecological, seasonal and demographic characteristics of the host. By doing so, it is possible to assess the role of both local and global properties of contact networks in disease dynamics.
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As reported in previous studies [, ], contact patterns may exhibit important het- erogeneities at diﬀerent time scales relevant for disease transmission. At the ﬁnest scale of minutes and hours, our data display strong ﬂuctuations driven by the circadian activities of individuals, as one may easily expect. Whether such ﬂuctuations should be expected also at the daily or longer time scale, remains an open question. To address this issue, we performed a longitudinal analysis of the contact networks extracted from the tags, with the main goal of measuring potential similarities between contacts measured from house- holds on diﬀerent days, keeping in mind the constraints imposed by the short study dura- tion. These results indicate that the observed contact patterns of each household member were signiﬁcantly similar from day to day; at the level both of contacted individuals (as measured by the loyalty) and of the durations of time spent in contact with diﬀerent indi- viduals (as measured by the cosine similarity). Overall, within-household contacts appear to be highly stable and repetitive across single days, thus suggesting that a short data collec- tion period of a few days could be suﬃcient for an accurate description. Such information is relevant to understand how much a single experimental day can be considered repre- sentative of the typical contact patterns within a household and how much data gathering would be needed to obtain a comprehensive picture of the full contact network. On the other hand, contacts across households, mainly driven by adults who could thus act as bridges in transmission of communicable diseases from one household to the other, ap- pear to be irregular and quite diﬃcult to capture during a time window of a week or less. While between-household contacts could be a signiﬁcant driver of infectious diseases, the sample size does not allow obtaining any deﬁnitive insights on the mixing behaviour of the population in general. Furthermore, our results support the role of children in transmit- ting respiratory infections within the household [, , ].
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Despite the limitations described above, the present study emphasizes the effects of contact heterogeneity on the dynamics of communicable diseases. On the one hand, the small differences between simulated spread on both the HET and DYN networks shows that taking into account the very detailed actual time ordering of the con- tacts between individuals, with a time resolution of min- utes, does not seem to be essential to describe disease spread on a timescale of several days or weeks. On the other hand, the large differences in disease spread in the HOM network emphasize the need to include detailed information about the heterogeneity of contact duration (compared with an assumption of homogeneity) to model disease spread, as also found previously [12,13] for simulations of disease spread dynamics based on diary- based survey data. Results from the different procedures for data extension also showed how the rate of new con- tacts is a very important parameter [8,12,43]. Overall, the combined comparison of the spreading processes simu- lated on the HET, DYN and HOM networks and using the different data-extension procedures gave an impor- tant assessment of the level of detail concerning the con- tact patterns of individuals that is needed to inform modeling frameworks of epidemic spread.
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complaints by the users. In Pakistan, a huge number of subscribers use theses web sites for acquiring the data and accessing multimedia information on daily bases. Even these social sites have been used for commercial and non-commercial proposes. Many social applications and services facilitate the user for collecting available online information and interaction between database domains. Facebook and YouTube have investigated and highlighted the potential issues on these social network sites. Facebook and You- Tube are the most widespread applications used throughout the world where users re- ceive and disseminate their important information. Billions of users have observed on social networks and a vast number of users choose Facebook and YouTube for obtain- ing and transmitting their data and multimedia services. Both social web sites provide user friendly interfaces to share their information among various social groups and so- cieties on Internet. These social networks provide many benefits to their users for ex- ample easy interface, fast data transmission, vast bandwidth, vast coverage and unin- terrupted communication channels. On the other sites user faced some serious issues on these social web sites. The rest of this paper is planned as follows: Section 2 offers an overview of computer networks; Section 3 classifies the usage of social computing ap- plication; Section 4 gives a brief overview of the Facebook and YouTube; Section 5 sta- tistically analyzes the potential issues related with social computing application and Section 6 draws the conclusion.
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Although many network models exhibit infinite topological dimension (d), simple mean-field ap- proximations cannot capture several important features [23–28]. Very recently it has been conjectured [29–31] that generic slow (power-law, or logarithmic) dynamics is observable only in networks with finite d. This claim is relevant in the light of recent developments of dynamical processes on complex networks such as the simple model of “working memory” , brain dynamics , social networks with heterogeneous communities , or to understand the slow relaxation in glassy systems . Slow dynamics has been shown to originate from the bursty behavior of the agents connected by small world networks resulting in memory effects . On the other hand it can also be related to arbitrarily large (l < N), correlated rare-regions (RR), which possess long lifetime in the inactive phase, above the pure critical point λ 0 c < λ < λ c . This can be understood by non-perturbative methods [36–41].
As part of the its 3300 ICP solution, RGA selects the Mitel Networks 6500 Unified Communications to enable the sales team to ensure customer satisfaction by efficiently managing voice, email, and fax messages. With a single message store, the 6500 Unified Messaging combines voice mail, email, and faxes in one inbox, which users can navigate with natural speech commands. This ability provides users with the flexibility to manage messages by sender, date, or type; forward or reply to the messages with voice; or to simply return a call without having to look up the number. To further enhance productivity, users can check the calendar, make appointments and meeting requests, and create tasks through the voice user interface. Managing messages in this way while on the road or in the office affords RGA’a sales team the advantage of conducting business anywhere, anytime. Next, to facilitate and cut costs of troubleshooting assembly lines and desktops, RGA decides to provide in-building mobility to its technicians. The 3300 ICP provides full communications mobility by supporting Symbol Spectrum24 and NetVision, to make and receive calls from anywhere in their facility to consult with colleagues about a problem as they are working to resolve it. This mobility ensures efficient problem resolution and helps RGA spend less on technician hours. Moreover, because the system is IP based, technicians can easily check the trouble ticket database to ensure that the next ticket they solve is a high priority; thus, their time is spent primarily on problems with the highest business impact.
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Social media marketing involves targeting the target audience through social networks by creating content that matches the interests of a particular user group and fits into the specifics of the functioning of a particular social service. The main goal of the company is to develop an effective strategy that will optimize its activities in the social network, using the best tools for attracting users and building sustainable communication on a long-term basis, with the effective use of available financial and human resources. A company to ensure competitive advantages in social networks should analyze the specified environment and respond promptly to changes in user behavior, the introduction of innovative digital marketing technologies, transformation into relevant services through the implementation of appropriate marketing decisions.
Since Green and Tobolsky  numerous models have been presented in an at- tempt to capture the characteristic behaviors of telechelic polymer networks [66, 65, 55, 62, 63, 64, 48, 49, 50] . An early history of constitutive models is well covered in the introduction of Tripathi et al.  and a thorough discussion in Wang and Larson  provides a recent update. Of present interest is the work done by Vac- caro and Marrucci in . Based on theory and simulation results from van den Brule and Hoogerbrugge  , they model this system as finitely extensible dumbbells separated into two groups, active and dangling. Each segment has its own charac- teristics and thus effects the polymer network structure differently. In simulation, dumbbells stochastically switch between states. Representing each state as a sin- gle Fokker-Planck equation creates a system of equations which can be solved using closure approximations. A systematic evaluation of this approach is undertaken by . On the other hand, Hernández Cifre  takes a Brownian Dynamics approach. Instead of Fokker-Plank equations, a Langevin equation describes the micro-scale dy- namics of each dumbbell type. Macro-scale terms, such as the fluid stress, are then determined by averaging over many realizations -typically ensembles of 5000 dumb- bells. In this way, Hernández Cifre shows the viscosity profile of associative polymers in simple shear flow can be captured without the closure approximations needed in other approaches.
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MSNs rely on a wide range of short-range wireless tech- nologies to form temporal ad hoc networks for opportunistic communications , . Therefore, the mobility of mobile devices will directly affect the topology of the network and the quality of the communication. MSNs have high mobile characteristics, and the network structure changes with the trajectory of human movement. Temporal characteristics can- not be ignored in MSNs analysis. Some studies have tried to study time-varying networks based on time-varying graphs, and focus on quantifying the impact of temporal properties on time-varying networks. Time-varying graphs are a natural model, which can effectively reflect the social relationship between nodes, and extend the concept of connectivity and the definition of graphical components to the time dimension. Therefore, in the following parts, we will first explore time- varying graphs and their applications, and then we will give a detailed survey about exploiting MSNs from the temporal perspective, i.e., temporal social property and temporal social properties-based applications.
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