27 research outputs found
Burstiness and spreading on temporal networks
We discuss how spreading processes on temporal networks are impacted by the
shape of their inter-event time distributions. Through simple mathematical
arguments and toy examples, we find that the key factor is the ordering in
which events take place, a property that tends to be affected by the bulk of
the distributions and not only by their tail, as usually considered in the
literature. We show that a detailed modeling of the temporal patterns observed
in complex networks can change dramatically the properties of a spreading
process, such as the ergodicity of a random walk process or the persistence of
an epidemic.Comment: 5 page
Infrequent social interaction can accelerate the spread of a persuasive idea
We study the spread of a persuasive new idea through a population of
continuous-time random walkers in one dimension. The idea spreads via social
gatherings involving groups of nearby walkers who act according to a biased
"majority rule": After each gathering, the group takes on the new idea if more
than a critical fraction of them
already hold it; otherwise they all reject it. The boundary of a domain where
the new idea has taken hold expands as a traveling wave in the density of new
idea holders. Our walkers move by L\'{e}vy motion, and we compute the wave
velocity analytically as a function of the frequency of social gatherings and
the exponent of the jump distribution. When this distribution is sufficiently
heavy tailed, then, counter to intuition, the idea can propagate faster if
social gatherings are held less frequently. When jumps are truncated, a
critical gathering frequency can emerge which maximizes propagation velocity.
We explore our model by simulation, confirming our analytical results
Navigability of temporal networks in hyperbolic space
Information routing is one of the main tasks in many complex networks with a
communication function. Maps produced by embedding the networks in hyperbolic
space can assist this task enabling the implementation of efficient navigation
strategies. However, only static maps have been considered so far, while
navigation in more realistic situations, where the network structure may vary
in time, remain largely unexplored. Here, we analyze the navigability of real
networks by using greedy routing in hyperbolic space, where the nodes are
subject to a stochastic activation-inactivation dynamics. We find that such
dynamics enhances navigability with respect to the static case. Interestingly,
there exists an optimal intermediate activation value, which ensures the best
trade-off between the increase in the number of successful paths and a limited
growth of their length. Contrary to expectations, the enhanced navigability is
robust even when the most connected nodes inactivate with very high
probability. Finally, our results indicate that some real networks are
ultranavigable and remain highly navigable even if the network structure is
extremely unsteady. These findings have important implications for the design
and evaluation of efficient routing protocols that account for the temporal
nature of real complex networks.Comment: 10 pages, 4 figures. Includes Supplemental Informatio
Collective iteration behavior for online social networks
Understanding the patterns of collective behavior in online social network (OSNs) is critical to expanding the knowledge of human behavior and tie relationship. In this paper, we investigate a specific pattern called social signature in Facebook and Wiki usersâ online communication behaviors, capturing the distribution of frequency of interactions between different alters over time in the ego network. The empirical results show that there are robust social signatures of interactions no matter how friends change over time, which indicates that a stable commutation pattern exists in online communication. By comparing a random null model, we find the that commutation pattern is heterogeneous between ego and alters. Furthermore, in order to regenerate the pattern of the social signature, we present a preferential interaction model, which assumes that new users intend to look for the old users with strong ties while old users have tendency to interact with new friends. The experimental results show that the presented model can reproduce the heterogeneity of social signature by adjusting 2 parameters, the number of communicating targets m and the max number of interactions n, for Facebook users, m=n=5, for Wiki users, m=2 and n=8. This work helps in deeply understanding the regularity of social signature
Navigability of temporal networks in hyperbolic space
Information routing is one of the main tasks in many complex networks with a communication function. Maps produced by embedding the networks in hyperbolic space can assist this task enabling the implementation of efficient navigation strategies. However, only static maps have been considered so far, while navigation in more realistic situations, where the network structure may vary in time, remains largely unexplored. Here, we analyze the navigability of real networks by using greedy routing in hyperbolic space, where the nodes are subject to a stochastic activation-inactivation dynamics. We find that such dynamics enhances navigability with respect to the static case. Interestingly, there exists an optimal intermediate activation value, which ensures the best trade-off between the increase in the number of successful paths and a limited growth of their length. Contrary to expectations, the enhanced navigability is robust even when the most connected nodes inactivate with very high probability. Finally, our results indicate that some real networks are ultranavigable and remain highly navigable even if the network structure is extremely unsteady. These findings have important implications for the design and evaluation of efficient routing protocols that account for the temporal nature of real complex networks