Degree Correlations Amplify the Growth of Cascades in Networks
| Autor: | Peter G. Fennell, Xin-Zeng Wu, Allon G. Percus, Kristina Lerman |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Physics - Physics and Society Computer science Assortativity Network structure FOS: Physical sciences Computer Science - Social and Information Networks Physics and Society (physics.soc-ph) Degree distribution 01 natural sciences 010305 fluids & plasmas Cascade 0103 physical sciences Statistical physics 010306 general physics |
| Popis: | Networks facilitate the spread of cascades, allowing a local perturbation to percolate via interactions between nodes and their neighbors. We investigate how network structure affects the dynamics of a spreading cascade. By accounting for the joint degree distribution of a network within a generating function framework, we can quantify how degree correlations affect both the onset of global cascades and the propensity of nodes of specific degree class to trigger large cascades. However, not all degree correlations are equally important in a spreading process. We introduce a new measure of degree assortativity that accounts for correlations among nodes relevant to a spreading cascade. We show that the critical point defining the onset of global cascades has a monotone relationship to this new assortativity measure. In addition, we show that the choice of nodes to seed the largest cascades is strongly affected by degree correlations. Contrary to traditional wisdom, when degree assortativity is positive, low degree nodes are more likely to generate largest cascades. Our work suggests that it may be possible to tailor spreading processes by manipulating the higher-order structure of networks. 9 pages, 8 figures |
| Databáze: | OpenAIRE |
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