Importance of individual events in temporal networks

Takaguchi, Taro; Sato, N.; Yano, Kazuo; Masuda, Naoki

doi:10.1088/1367-2630/14/9/093003

Cited by 44 publications

(40 citation statements)

References 48 publications

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“…In addition, by using TCC, we show that the remaining part of the temporal network is highly redundant in the sense that there are many ways to send information as quickly as possible. Although these properties are recognized in the network science community [32][33][34], we quantitatively confirm it for the first time using our centrality notions. We also demonstrate that the removal of temporal vertices according to their TBCC values is effective for hindering the propagation of information for both delaying and stopping it.…”

Section: Introductionsupporting

confidence: 57%

“…Although such structural redundancy in temporal networks was suggested in some previous studies [32][33][34], our centrality notions enable us to clearly quantify and visualize this property. We believe that the centrality notions we proposed are useful for further studying the structure of temporal networks and verifying generative models of temporal networks.…”

Section: Discussionmentioning

confidence: 87%

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Coverage centralities for temporal networks

2016

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Abstract. Structure of real networked systems, such as social relationship, can be modeled as temporal networks in which each edge appears only at the prescribed time. Understanding the structure of temporal networks requires quantifying the importance of a temporal vertex, which is a pair of vertex index and time. In this paper, we define two centrality measures of a temporal vertex based on the fastest temporal paths which use the temporal vertex. The definition is free from parameters and robust against the change in time scale on which we focus. In addition, we can efficiently compute these centrality values for all temporal vertices. Using the two centrality measures, we reveal that distributions of these centrality values of realworld temporal networks are heterogeneous. For various datasets, we also demonstrate that a majority of the highly central temporal vertices are located within a narrow time window around a particular time. In other words, there is a bottleneck time at which most information sent in the temporal network passes through a small number of temporal vertices, which suggests an important role of these temporal vertices in spreading phenomena.

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Section: Introductionsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 87%

Coverage centralities for temporal networks

2016

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“…In time-varying networks the analytical study of contagion processes is hindered by the difficulties in dealing with the concurrent time scales of the contagion and network evolution processes. [34][35][36][37][38][39]. In the case of activity driven networks however it is possible to derive the mean-field level dynamical equations describing the contagion process by defining the activity block variable I t a and S t a that represent the number of infected and susceptible individuals, respectively, in the class of activity a at time t. From those quantity it is possible to derive the mean-field evolution of the number of infected individuals of class a at time t + 1 as…”

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confidence: 99%

Controlling Contagion Processes in Activity Driven Networks

et al. 2014

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The vast majority of strategies aimed at controlling contagion processes on networks considers the connectivity pattern of the system as either quenched or annealed. However, in the real world many networks are highly dynamical and evolve in time concurrently to the contagion process. Here, we derive an analytical framework for the study of control strategies specifically devised for time-varying networks. We consider the removal/immunization of individual nodes according the their activity in the network and develop a block variable mean-field approach that allows the derivation of the equations describing the evolution of the contagion process concurrently to the network dynamic. We derive the critical immunization threshold and assess the effectiveness of the control strategies. Finally, we validate the theoretical picture by simulating numerically the information spreading process and control strategies in both synthetic networks and a large-scale, real-world mobile telephone call dataset.The spreading of infectious diseases, malwares, scientific ideas, memes are just few example of real world phenomena that can be modeled as contagion processes on networks [1][2][3][4]. It has long been acknowledged that network structures and connectivity patterns are a relevant factor in determining the properties of spreading processes. A number of strategies aimed at controlling them have been proposed with the aim of improving on the random removal strategies that originally defined the concept of herd immunity. Those strategies target nodes according the number of connections (degree), the k-core or the betweenness of nodes, just to mention a few examples [5,6]. The efficiency of each strategy is then measured by its effect on the contagion process when a fraction p of nodes is removed from the system. More precisely, the smaller is the fraction p of nodes removed in order to halt the contagion process and the more effective is the strategy. Although most real world networks show a high level of dynamic activity, the large majority of theoretical results concerning the control of contagion processes have been obtained by using a complete timescale separation between the contagion process, τ P , and the change in network's structure, τ G . In these approaches the dynamical process takes place in either static (τ P τ G ) or annealed (τ P τ G ) networks. However, when the two time scales are comparable these convenient approximations might introduce uncontrolled biases in the correct characterization of the dynamical properties of the contagion process . Here we investigate the effect of time-varying connectivity pattern of networks on contagion control strategies by considering the specific class of activity driven network models [23]. In particular, we consider the susceptibleinfected-susceptible (SIS) contagion model [30] and derive analytically its critical immunization threshold in three different control strategies. We also validate the findings obtained in synthetic networks by studying the effect of each strategy...

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“…We apply our bootstrapping method to the empirical records of face-to-face interaction between anonymized employees in two company offices in Japan [5], [6], which were obtained by World Signal Center, Hitachi, Ltd., Japan. The two data sets D 1 and D 2 contain 118, 656 and 274, 308 events between 163 and 211 employees over 73 and 120 days with 1 minute resolution, respectively.…”

Section: Results and Conclusionmentioning

confidence: 99%

Inferring Directed Static Networks of Influence from Undirected Temporal Networks

Takaguchi

Sato

Yano

et al. 2013

2013 IEEE 37th Annual Computer Software and Applications Conference

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A temporal network consists of a time series of interaction events, each of which is defined by a triplet composed of the indices of two nodes and the time of the event. Mapping a temporal network to a more tractable static network is often useful. A mapping method was recently proposed on the basis of the so-called transfer entropy (G. V. Steeg and A. Galstyan, in Proc. the 21st Int. Conf. WWW, p.509, 2012). In the proposed method, one generates the directed network of influence in which a directed link represents the causal relationship between activity patterns at two nodes. However, the significance of the inferred links and the sensitivity of results to the parameter values are still unclear. We propose a bootstrap sampling method to statistically configure the directed network of influence. We apply our method to the face-to-face interaction logs between office workers in Japanese companies. In particular, we find that the node centrality values are little correlated with the formal status of the nodes in the companies.

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Importance of individual events in temporal networks

Cited by 44 publications

References 48 publications

Coverage centralities for temporal networks

Coverage centralities for temporal networks

Controlling Contagion Processes in Activity Driven Networks

Inferring Directed Static Networks of Influence from Undirected Temporal Networks

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