Research Article | Open Access

# Effect of Heterogeneity of Vertex Activation on Epidemic Spreading in Temporal Networks

**Academic Editor:**Linying Xiang

#### Abstract

Development of sensor technologies and the prevalence of electronic communication services provide us with a huge amount of data on human communication behavior, including face-to-face conversations, e-mail exchanges, phone calls, message exchanges, and other types of interactions in various online forums. These indirect or direct interactions form potential bridges of the virus spread. For a long time, the study of virus spread is based on the aggregate static network. However, the interaction patterns containing diverse temporal properties may affect dynamic processes as much as the network topology does. Some empirical studies show that the activation time and duration of vertices and links are highly heterogeneous, which means intense activity may be followed by longer intervals of inactivity. We take heterogeneous distribution of the node interactivation time as the research background to build an asynchronous communication model. The two sides of the communication do not have to be active at the same time. One derives the threshold of virus spreading on the communication mode and analyzes the reason the heterogeneous distribution of the vertex interactivation time suppresses the spread of virus. At last, the analysis and results from the model are verified on the BA network.

#### 1. Introduction

The network topology which is formed by the interaction between individuals plays a fundamental role in the process of determining the epidemic spread [1–6]. The original study of epidemiology [7] is based on homogeneous mixing hypothesis, assuming that all people have the same opportunity to contact other individuals in the populations. The assumption and the corresponding results were challenged by empirical studies. The interactions in the populations can use a meaningful network structure to better describe [8]. A large number of empirical studies show that the node degree distribution in many of reality networks obeys heavy-tailed power-law distribution, which is conducive to the spread of virus.

Communication between individuals is the basis of the human society. Nowadays technology, such as sensor devices and online communication services, provides us with a large number of records of interaction between individuals, including face-to-face meetings, e-mail, and telephone communication [9–11]. A traditional way to describe these data is to represent them as an aggregate static network, in which an edge is established if interaction between the two ends of it took place at least once [8].

Another richer representation of this type of data is the temporal network model [12–21], in which the connection between two nodes only exists at the time of an event. A large number of these data usually consist of a sequence of interactive events. Every event is a triplet, that is, the IDs of two individuals involved in the event and the time of the event. Some studies of the temporal network focused on the impact of interevent time bursty on the spread of information or virus.

However, many human interactions are not always face-to-face or synchronous communication mode, such as e-mail exchange, short message, Twitter, and WeChat. Not all sent information can be accepted by the recipient, such as the recipient refusing to open a suspicious mail or refusing to click the link received. When a node sends a message to another node at the time , node in its active time decides whether to accept this message, where . What effect does the heterogeneous distribution of individual interactivation time have on the asynchronous information transmission and virus propagation and how does the heterogeneous behavior pattern of individuals impact on asynchronous transmission? These issues are worthy of attention and research.

#### 2. Model

In some temporal network literature, any two active nodes are likely to build a temporal edge. But the reality is that the neighbor nodes of a node are at a certain scope. The various factors decide the range of an individual contact, such as geographical areas of individual activity, the social circle of individual life and learning, kinship, and hobby. These links are established between one node and each of its possible interaction nodes, which constitutes a static aggregation network to descript node activity range. The network is denoted by . An e-mail exchange system, for example, can constitute a static network, which its nodes are formed by e-mail account address in system and edges are established between each e-mail user and users of his or her e-mail address list. So in the network, the vast majority of activities are carried out between the adjacent nodes. When a node is activated, it can interact with its neighbors rather than any other node in the network. The static network topology and node activation sequence properties affect the spread behavior on networks together.

The mathematical epidemiological model that is probably the most widely used for theorizing about and emulating epidemics is the so-called SIR (susceptible-infected-recovered) model. In the SIR model, with which we are concerned in the present report, each individual belongs to either an S (susceptible), I (infected), or R (recovered) state at any given time. When a susceptible individual contacts an infected individual, the former may be infected at an infection rate.

In our model, an action of an individual, such as sending a short message or receiving an e-mail, is called an activation event of the node. There is a difference in meaning between the interactivation time of a node and the interevent time of an edge. The former is based on the behavior of an individual and the latter is based on the interaction between two individuals.

In the model of the bursts of node interactivation time from recent literature [19], at each time point, an activated node chooses randomly another activated node to build an edge between them. If one of the two nodes is I state node and the other is S state node, the I state node will infect the S state node with some probability. Clearly, the model and the previous models have one thing in common, that is, the synchronous interaction, such as phone call, video meeting, and real-time files. However, many cases are closer to the asynchronous communication, such as e-mail exchange, SMS, Twitter, BBS, and other network communication ways, which the two sides of the communication can be active at different times. At each time , each active node in the model can accept from neighboring nodes some information or send some information to a neighbor. In reality, user may send or receive a group of information to or from more users at the same time. For simplicity, as long as the time scale is small enough, it can be considered that information is only sent to one of its adjacent nodes from an activated node at a time.

In our model, all nodes are S state at initial moment except for a node which is I state. When the initial infected node is activated, it chooses randomly one of its neighbor nodes and sends node a message containing infection content no matter whether node is currently activated. Then the node becomes inactive state at the next time. At each time , every activated node will accept one or more messages containing virus in accordance with a certain probability for each message of them and then change from S state to I state at the next moment if it has received messages containing virus sent from its neighbor nodes and the node is S state before time . If the activated node is I state, it will choose a neighbor from some address book, such as e-mail address book, the telephone communication book, or MSN friends list, to send a message containing virus. At each time , an infected node recovers to R state with some probability.

If an S state node receive messages containing viruses from other nodes, it is at the risk of infection. To facilitate the narrative of node state transition in the model, we introduce a new state, which is denoted by D (dangerous). An S state node change into a D state node. When it receive the message containing virus. When a D state node is activated, it has the potential to accept this suspicious message and then its state changes from D to I.

As shown in Figure 1, each activated node is subject to the following rules at each time .(1)If the node is I state, it sends a message containing viruses to its neighbor node randomly chosen. If the node is S state at present, it becomes D state at the next moment ; if the node is D state, I state, or R state, it will maintain the current state.(2)If the node is D state, that is, it received one or more messages containing viruses from neighbors at some point , it turns into I state if it accepted the message with probability , in which the transmission time delay is ; it recovers to S state if it refuses to accept the message with probability .(3)If the node is in the S state or R state, any action from it will not be considered. The sent message that does not contain virus does not affect the propagation process of virus and therefore is not considered in the model. At each time , no matter whether the node is activated, it is subject to the following rule:(4)if it is I state node, it will be restored to R state with probability .

In the second point, we assume that if a user saw the suspicious messages, suspicious information, or suspicious links and refused to accept them for the first time, then he or she will never accept them. So, the corresponding node state can be changed into S state from D state at the next moment.

In many types of empirical data, a wide range of patterns of human activity are known to exhibit long-tailed dynamics [22–24]. Here, we model the node interactivation time heavy-tailed distribution with the power law distribution. Node interactivation time obeys power-law distribution with lower bound [25]: where is a lower bound of node interactivation time and is the exponent or scaling parameter of the power-law distribution.

#### 3. Epidemic Threshold

Key quantities for epidemic dynamics are the so-called transmissibility and the secondary reproductive number [26]. is the probability that an infected individual would transmit virus to a susceptible neighbor before it recovers, and is the expected number of new nodes infected by infected nodes.

An infected node is restored into recovered state within a time step with the probability of , which obeys the binomial distribution of the mean for . So the average time that an infected node of network changes into a recovered node is . When an infected node is activated, it will randomly select its neighborhood to send information containing virus. The neighbor accepts the information at some futural time with probability of and will be infected as a consequence if it was previously S state. The interactivation time for each node of network is subject to identically independent distribution. According to the theory of update [27], the transmissibility for the dynamics can be obtained as where , is to generate time distribution [28], is the mean of node interactivation time, and is the density distribution function of node interactivation time . Where the node interactivation time obeys power-law distribution with exponent , given by (1), transmissibility can be written as

When we arrive at a node by following a random chosen edge, the number of remaining edges of the node excluding we along is denoted by . When a node is infected by its neighbor node , node selects randomly one of its neighbor nodes as the spread object and the probability that the selected node is not node is . Thus the reproductive number equals in our model, where is the average remaining degree of network nodes. It can be expressed by node average degree and the second moment of node degrees [26, 29]; that is, . Hence the reproductive number . A necessary condition for virus epidemic on network is that the reproductive number must be greater than one; combined with (3), we can obtain the epidemic threshold as (for derivation see the Appendix) where , which is the effective transmission rate of virus, is epidemic threshold, and . Parameter is only related to the structure of the static network and has nothing to do with the dynamic activation properties of nodes.

#### 4. Results and Analysis

Under the condition of nodes dynamic activation, the characteristics of the epidemic threshold of virus are analyzed firstly. BA network [30] is in a typical heterogeneous structure network. Each new node connects existing nodes of the network and the final total number of the network nodes is . For a limited scale of BA network [31], the node degree distribution , the node average degree , and the node maximum degree . We can get the parameter of BA network as

In Figure 2, the epidemic threshold of virus is calculated by (4) and (5) according to the following conditions: the static network G is the BA network of node average degree for 10, the total number of nodes , the node interactivation time obeys power-law distribution given by (1), the minimum value of node interactivation time , and node average recovery time was shown in the illustration in Figure 2.

We can see from Figure 2 three points. First, the epidemic threshold becomes larger as the more heterogeneous node interactivation time distribution is (i.e., decrease) for different average recovery time of infected node. The smaller the power-law exponent of node interactivation time distribution is, the greater the average value of node interactivation time derived by (1) is, that is, the fewer the average times of node activation are at the same time. That means an infected node has less chance to spread virus to its adjacent nodes before it recovers. Thus only high effective transmission rate of virus ensures its epidemic under the circumstances. Second, the greater the average recovery time of infected node is, which means infected nodes have more chance to be activated and transmit virus to their adjacent nodes, therefore the smaller the epidemic threshold is. Thirdly, as the power-law exponent of node interactivation time increases, propagation threshold is tending to the same value no matter what value node average recovery time is. The increase of the power-law exponent of node interactivation time makes the heterogeneity and mean of diminish so that there are a large number of nodes of network activated at every moment. Until most of the nodes remain active, dynamic activation network gradually closes to the static network . In this case, epidemic threshold on temporal network is only related to the topology of cumulative static network , which can be proved from (4).

One simulates the model on the static BA network, which its scale is 5000 nodes and each new node connects existing 5 nodes of network. A randomly selected node is set initially to infected state, namely, seed node. The average recovery probability of infected nodes is 0.1. The node interactivation time obeys the power-law distribution forms of (1) and the minimum value of node interactivation time . The exponent is 2.1, 2.5, and 3.0, respectively. Figure 3 shows the node density infected by virus change along with virus transmission rate . It is observed that the stronger the heterogeneity of node interactivation time is, the greater the epidemic threshold of virus is and the less the final spread scope of virus is. The node density infected by virus change along with time in Figure 4. As Figure 4 shows, the stronger the heterogeneity of node interactivation time is, the slower the spread speed of the virus is. That the heterogeneity of node interactivation time inhibits the propagation of virus is illustrated from two different aspects of the scale and the speed of virus propagation, respectively, in Figures 3 and 4, which demonstrate that the data simulation results accord with the theoretical analysis results of Figure 2.

#### 5. Conclusion

Differring from previous studies that the heterogeneous of interevent time distribution affects the spread of the virus on networks, this work is based on the heterogeneous distribution of node interactivation time and establishes the asynchronous communication model, which is more obviously universal than the former. Asynchronous interaction style is suitable for the case that the two sides of interaction are not always active at the same time, which is prevailing in the applications from Internet and mobile Internet. Where node interactivation time follows power-law distribution, epidemic threshold of the model is deduced by means of the theory of updates. Simulating in BA network, it is concluded that the stronger the heterogeneity of node interactivation time is, the greater the epidemic threshold of virus is and the smaller the scale and speed of virus propagation are, which is consistent with the results of threshold theoretical derivation.

In this work, asynchronous communication is elaborated by means of the example of sending and receiving e-mails and messages, and epidemic threshold is derived by using the power-law distribution as the heterogeneous distribution of node interactivation time. But time statistics of human behavior is far from being so simple. Different data sets, such as the data sets from mobile phone text messages, blog, BBS, and online services, have different heterogeneous time distribution of individual behavior [21], so the time distribution of individual behavior itself is a complicated and worth studying issue.

#### Appendix

#### The Derivation of Epidemic Threshold

The average value of node interactivation time can be calculated by (1) with where .

* The First Case (**).* The integral variable of (2) satisfies condition , so one can get the conclusion . Generated time distribution is calculated as

By using (2), the transmissibility for the dynamics can be obtained as

From the elaboration on the reproductive number from main text, we can know that the two conditions and are satisfied. Combined with (A.3), we can get the following condition: where .

Effective transmission rate of virus is defined as . Epidemic threshold is the minimum value of where virus is epidemic in network. We can get by (A.4)

So, epidemic threshold can be gained as where , , and .

* The Second Case (**).* Generated time distribution is calculated by (1) as
By using (2), the transmissibility for the dynamics can be obtained as

The reproductive number is satisfied with the two conditions and . Combined with (A.8), we can get the following condition: where .

We can get by (A.4)

So, epidemic threshold is where , , and .

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grant nos. 61133016, 61163066, and 60902074) and in part by the National High Technology Joint Research Program of China (863 Program, Grant no. 2011AA010706).

#### References

- M. J. Keeling and K. T. D. Eames, “Networks and epidemic models,”
*Journal of the Royal Society Interface*, vol. 2, no. 4, pp. 295–307, 2005. View at: Publisher Site | Google Scholar - S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, “Complex networks: structure and dynamics,”
*Physics Reports*, vol. 424, no. 4-5, pp. 175–308, 2006. View at: Publisher Site | Google Scholar | MathSciNet - S. Aral, L. Muchnik, and A. Sundararajan, “Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks,”
*Proceedings of the National Academy of Sciences of the United States of America*, vol. 106, no. 51, pp. 21544–21549, 2009. View at: Publisher Site | Google Scholar - A. Barrat, M. Barthélemy, and A. Vespignani,
*Dynamical Processes on Complex Networks*, Cambridge University Press, Cambridge, 2008. View at: MathSciNet - E. Volz and L. A. Meyers, “Epidemic thresholds in dynamic contact networks,”
*Journal of the Royal Society Interface*, vol. 6, no. 32, pp. 233–241, 2009. View at: Publisher Site | Google Scholar - N. A. Christakis and J. H. Fowler, “The spread of obesity in a large social network over 32 years,”
*The New England Journal of Medicine*, vol. 357, no. 4, pp. 370–379, 2007. View at: Publisher Site | Google Scholar - R. M. Anderson, R. M. May, and B. Anderson,
*Infectious Diseases of Humans: Dynamics and Control*, vol. 28, Wiley Online Library, 1992. - M. E. J. Newman,
*Networks: An Introduction*, Oxford University Press, Oxford, UK, 2010. View at: MathSciNet - C. Cioffi-Revilla, “Computational social science,”
*Wiley Interdisciplinary Reviews: Computational Statistics*, vol. 2, no. 3, pp. 259–271, 2010. View at: Publisher Site | Google Scholar - A. Vespignani, “Modelling dynamical processes in complex socio-technical systems,”
*Nature Physics*, vol. 8, no. 1, pp. 32–39, 2012. View at: Publisher Site | Google Scholar - C. T. Butts, “A relational event framework for social action,”
*Sociological Methodology*, vol. 38, no. 1, pp. 155–200, 2008. View at: Publisher Site | Google Scholar - P. Holme and J. Saramäki, “Temporal networks,”
*Physics Reports*, vol. 519, no. 3, pp. 97–125, 2012. View at: Publisher Site | Google Scholar - N. Masuda and P. Holme, “Predicting and controlling infectious disease epidemics using temporal networks,”
*F1000prime Reports*, vol. 5, article 6, 2013. View at: Publisher Site | Google Scholar - P. Holme and J. Saramäki, “Temporal networks as a modeling framework,” in
*Temporal Networks*, pp. 1–14, Springer, 2013. View at: Google Scholar - T. Takaguchi, N. Masuda, and P. Holme, “Bursty communication patterns facilitate spreading in a threshold-based epidemic dynamics,”
*PLoS ONE*, vol. 8, no. 7, Article ID e68629, 2013. View at: Publisher Site | Google Scholar - N. Masuda, K. Klemm, and V. M. Eguíluz, “Temporal networks: slowing down diffusion by long lasting interactions,”
*Physical Review Letters*, vol. 111, Article ID 188701, 2013. View at: Publisher Site | Google Scholar - S. Lee, L. E. C. Rocha, F. Liljeros, and P. Holme, “Exploiting temporal network structures of human interaction to effectively immunize populations,”
*PLoS ONE*, vol. 7, no. 5, Article ID e36439, 2012. View at: Publisher Site | Google Scholar - F. Karimi and P. Holme, “A temporal network version of Watts’s cascade model,” in
*Temporal Networks*, pp. 315–329, Springer, 2013. View at: Google Scholar - L. E. Rocha, A. Decuyper, and V. D. Blondel, “Epidemics on a stochastic model of temporal network,” in
*Dynamics on and of Complex Networks, Volume 2*, Modeling and Simulation in Science, Engineering and Technology, pp. 301–314, Springer, 2013. View at: Publisher Site | Google Scholar - Z. Yang, A.-X. Cui, and T. Zhou, “Impact of heterogeneous human activities on epidemic spreading,”
*Physica A: Statistical Mechanics and Its Applications*, vol. 390, no. 23-24, pp. 4543–4548, 2011. View at: Publisher Site | Google Scholar | MathSciNet - T. Zhou, X.-P. Han, X.-Y. Yan et al., “Statistical mechanics on temporal and spatial activities of human,”
*Journal of University of Electronic Science and Technology of China*, vol. 42, no. 4, 2013. View at: Google Scholar - J.-P. Eckmann, E. Moses, and D. Sergi, “Entropy of dialogues creates coherent structures in e-mail traffic,”
*Proceedings of the National Academy of Sciences of the United States of America*, vol. 101, no. 40, pp. 14333–14337, 2004. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - A. Vázquez, J. G. Oliveira, Z . Dezsö, K.I. Goh, I. Kondor, and A. L. Barabási, “Modeling bursts and heavy tails in human dynamics,”
*Physical Review E*, vol. 73, no. 3, Article ID 036127, 2006. View at: Publisher Site | Google Scholar - A.-L. Barabási, “The origin of bursts and heavy tails in human dynamics,”
*Nature*, vol. 435, no. 7039, pp. 207–211, 2005. View at: Publisher Site | Google Scholar - A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,”
*SIAM Review*, vol. 51, no. 4, pp. 661–703, 2009. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - M. E. J. Newman, “Spread of epidemic disease on networks,”
*Physical Review E: Statistical, Nonlinear, and Soft Matter Physics*, vol. 66, no. 1, Article ID 016128, 11 pages, 2002. View at: Publisher Site | Google Scholar | MathSciNet - W. Feller,
*An Introduction to Probability Theory and Its Applications*, vol. 2, 1974. - A. Vazquez, “Polynomial growth in branching processes with diverging reproductive number,”
*Physical Review Letters*, vol. 96, no. 3, Article ID 038702, 2006. View at: Publisher Site | Google Scholar - M. E. J. Newman, S. H. Strogatz, and D. J. Watts, “Random graphs with arbitrary degree distributions and their applications,”
*Physical Review E: Statistical, Nonlinear, and Soft Matter Physics*, vol. 64, no. 2, Article ID 026118, 2001. View at: Google Scholar - A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,”
*American Association for the Advancement of Science*, vol. 286, no. 5439, pp. 509–512, 1999. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - R. Pastor-Satorras and A. Vespignani, “Epidemic dynamics in finite size scale-free networks,”
*Physical Review E: Statistical, Nonlinear, and Soft Matter Physics*, vol. 65, no. 3, Article ID 035108, 2002. View at: Publisher Site | Google Scholar

#### Copyright

Copyright © 2014 Yixin Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.