The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge-sharing and influence in online social networks via viral marketing
Communications of the ACM - Mobile computing opportunities and challenges
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Mining social networks using heat diffusion processes for marketing candidates selection
Proceedings of the 17th ACM conference on Information and knowledge management
Rumor spreading on random regular graphs and expanders
APPROX/RANDOM'10 Proceedings of the 13th international conference on Approximation, and 14 the International conference on Randomization, and combinatorial optimization: algorithms and techniques
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With the development of modern technology(communication, transportation, etc.), many new social networks have formed and influenced our life. The research of mining these new social networks has been used in many aspects. But compared with traditional networks, these new social networks are usually very large. Due to the complexity of the latter, few model can be adapted to mine them effectively. In this paper, we try to mine these new social networks using Wave Propagation process and mainly discuss two applications of our model, solving Message Broadcasting problem and Rumor Spreading problem. Our model has the following advantages: (1) We can simulate the real networks message transmitting process in time since we include a time factor in our model. (2) Our Message Broadcasting algorithm can mine the underlying relationship of real networks and represent some clustering properties. (3) We also provide an algorithm to detect social network and find the rumor makers. Complexity analysis shows our algorithms are scalable for large social network and stable analysis proofs our algorithms are stable.