Structure and dynamics of information pathways in online media
Proceedings of the sixth ACM international conference on Web search and data mining
Studying page life patterns in dynamical web
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Information diffusion in online social networks: a survey
ACM SIGMOD Record
Personalized influence maximization on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Diffusion of innovations revisited: from social network to innovation network
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
The bursty dynamics of the Twitter information network
Proceedings of the 23rd international conference on World wide web
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In networks, contagions such as information, purchasing behaviors, and diseases, spread and diffuse from node to node over the edges of the network. Moreover, in real-world scenarios multiple contagions spread through the network simultaneously. These contagions not only propagate at the same time but they also interact and compete with each other as they spread over the network. While traditional empirical studies and models of diffusion consider individual contagions as independent and thus spreading in isolation, we study how different contagions interact with each other as they spread through the network. We develop a statistical model that allows for competition as well as cooperation of different contagions in information diffusion. Competing contagions decrease each other's probability of spreading, while cooperating contagions help each other in being adopted throughout the network. We evaluate our model on 18,000 contagions simultaneously spreading through the Twitter network. Our model learns how different contagions interact with each other and then uses these interactions to more accurately predict the diffusion of a contagion through the network. Moreover, the model also provides a compelling hypothesis for the principles that govern content interaction in information diffusion. Most importantly, we find very strong effects of interactions between contagions. Interactions cause a relative change in the spreading probability of a contagion by em 71% on the average.