Strong ties promote the epidemic prevalence in susceptible-infected-susceptible spreading dynamics
arXiv:1311.5932 · doi:10.1371/journal.pone.0113457
Abstract
Understanding spreading dynamics will benefit society as a whole in better preventing and controlling diseases, as well as facilitating the socially responsible information while depressing destructive rumors. In network-based spreading dynamics, edges with different weights may play far different roles: a friend from afar usually brings novel stories, and an intimate relationship is highly risky for a flu epidemic. In this article, we propose a weighted susceptible-infected-susceptible model on complex networks, where the weight of an edge is defined by the topological proximity of the two associated nodes. Each infected individual is allowed to select limited number of neighbors to contact, and a tunable parameter is introduced to control the preference to contact through high-weight or low-weight edges. Experimental results on six real networks show that the epidemic prevalence can be largely promoted when strong ties are favored in the spreading process. By comparing with two statistical null models respectively with randomized topology and randomly redistributed weights, we show that the distribution pattern of weights, rather than the topology, mainly contributes to the experimental observations. Further analysis suggests that the weight-weight correlation strongly affects the results: high-weight edges are more significant in keeping high epidemic prevalence when the weight-weight correlation is present.
arXiv comment7 pages, 6 figures, and 1 Table. arXiv admin note: substantial text overlap with arXiv:1204.0100
References in corpus (19)
- Emergence of scaling in random networks
- Link Prediction in Complex Networks: A Survey
- Dynamical and correlation properties of the Internet
- How to project a bipartite network?
- Recommender Systems
- Solving the apparent diversity-accuracy dilemma of recommender systems
- Heat Conduction Process on Community Networks as a Recommendation Model
- Human dynamics revealed through Web analytics
- Tag-Aware Recommender Systems: A State-of-the-art Survey
- Uncovering collective listening habits and music genres in bipartite networks
- Information filtering via biased heat conduction
- Empirical analysis of web-based user-object bipartite networks
- Ultra accurate personalized recommendation via eliminating redundant correlations
- Recommendation model based on opinion diffusion
- Information filtering via preferential diffusion
- Extracting the information backbone in online system
- Behavior patterns of online users and the effect on information filtering
- Anchoring Bias in Online Voting
- Link power coordination for energy conservation in complex communication networks