Impact of Community Structure on Cascades

The threshold model is widely used to study the propagation of opinions and technologies in social networks. In this model, individuals adopt the new behavior based on how many neighbors have already chosen it. Specifically, we consider the permanent adoption model where individuals that have adopted the new behavior cannot change their state. We study cascades under the threshold model on sparse random graphs with community structure to see whether the existence of communities affects the number of individuals who finally adopt the new behavior. When seeding a small number of agents with the new behavior, the community structure has little effect on the final proportion of people that adopt it, i.e., the contagion threshold is the same as if there were just one community. On the other hand, seeding a fraction of the population with the new behavior has a significant impact on the cascade with the optimal seeding strategy depending on how strongly the communities are connected. In particular, when the communities are strongly connected, seeding in one community outperforms the symmetric seeding strategy that seeds equally in all communities. We also investigate the problem of optimum seeding given a budget constraint, and propose a gradient-based heuristic seeding strategy. Our algorithm, numerically, dispels commonly held beliefs in the literature that suggest the best seeding strategy is to seed over the nodes with the highest number of neighbors.Comment: Version to be published to EC 201

Impact of Community Structure on Cascades

International audienceThe threshold model is widely used to study the propagation of opinions and technologies in social networks. In this model individuals adopt the new behavior based on how many neighbors have already chosen it. We study cascades under the threshold model on sparse random graphs with community structure to see whether the existence of communities affects the number of individuals who finally adopt the new behavior. Specifically, we consider the permanent adoption model where nodes that have adopted the new behavior cannot change their state. When seeding a small number of agents with the new behavior, the community structure has little effect on the final proportion of people that adopt it, i.e., the contagion threshold is the same as if there were just one community. On the other hand, seeding a fraction of population with the new behavior has a significant impact on the cascade with the optimal seeding strategy depending on how strongly the communities are connected. In particular, when the communities are strongly connected, seeding in one community outperforms the symmetric seeding strategy that seeds equally in all communities

Threshold model of cascades in temporal networks

Threshold models try to explain the consequences of social influence like the spread of fads and opinions. Along with models of epidemics, they constitute a major theoretical framework of social spreading processes. In threshold models on static networks, an individual changes her state if a certain fraction of her neighbors has done the same. When there are strong correlations in the temporal aspects of contact patterns, it is useful to represent the system as a temporal network. In such a system, not only contacts but also the time of the contacts are represented explicitly. There is a consensus that bursty temporal patterns slow down disease spreading. However, as we will see, this is not a universal truth for threshold models. In this work, we propose an extension of Watts' classic threshold model to temporal networks. We do this by assuming that an agent is influenced by contacts which lie a certain time into the past. I.e., the individuals are affected by contacts within a time window. In addition to thresholds as the fraction of contacts, we also investigate the number of contacts within the time window as a basis for influence. To elucidate the model's behavior, we run the model on real and randomized empirical contact datasets.Comment: 7 pages, 5 figures, 2 table

Effect of zooplankton-mediated trophic cascades on marine microbial food web components (bacteria, nanoflagellates, ciliates)

To examine the grazing effects of copepod-dominated mesozooplankton on heterotrophic microbial communities, four mesocosm experiments using gradients of zooplankton abundance were carried out at a coastal marine site. The responses of different protist groups (nanoflagellates, ciliates) and bacterioplankton in terms of abundance and additionally, for bacteria, diversity, production, and exoenzymatic activity, were monitored during 1 week of incubation. Independent of the initial experimental abiotic conditions and the dominating copepod species, zooplankton caused order-of-magnitude changes in microbial functional groups in a clear community-wide four-link trophic cascade. The strongest predatory effects were observed for protist concentrations, thus generating inverse relationships between mesozooplankton and ciliates and between ciliates and nanoplankton. Copepod grazing effects propagated even further, not only reducing the abundance, production, and hydrolytic activity of bacterioplankton but also increasing bacterial diversity. The overall strength of this trophic cascade was dampened with respect to bacterial numbers, but more pronounced with respect to bacterial diversity and activity. High predation pressure by heterotrophic nanoflagellates, realized at the highest copepod abundance, was probably the underlying mechanism for these structural changes in the bacterial assemblages. Our results thus suggest a mechanism by which changes in higher trophic levels of marine plankton indirectly affect prokaryotic assemblages and microbially mediated ecosystem functions

Phantom cascades: The effect of hidden nodes on information diffusion

Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and sampling strategies, this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades. In this work we study the effect of hidden network structure on information diffusion processes. We characterise information cascades through activation paths traversing visible and hidden parts of the network. We quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error. Finally, we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network.Comment: Preprint submitted to Elsevier Computer Communication