logo

SCIENTIA SINICA Informationis, Volume 48 , Issue 11 : 1575-1588(2018) https://doi.org/10.1360/N112018-00081

Decentralized cascade dynamics modeling

More info
  • ReceivedApr 9, 2018
  • AcceptedMay 22, 2018
  • PublishedNov 14, 2018

Abstract


Funded by

国家重点研发计划项目(2017YFB0803302)

国家自然科学基金(61425016,61472400,61572473)


References

[1] Tatar A, Antoniadis P, Amorim M D. From popularity prediction to ranking online news. Soc Netw Anal Min, 2014, 4: 174 CrossRef Google Scholar

[2] Tatar A, Antoniadis P, De Amorim M D, et al. Ranking news articles based on popularity prediction. In: Proceedings of International Conference on Advances in Social Networks Analysis and Mining, Istanbul, 2012. 106--110. Google Scholar

[3] Chen X, Zhang X D. A popularity-based prediction model for web prefetching. Computer, 2003, 36: 63--70. Google Scholar

[4] Famaey J, Iterbeke F, Wauters T. Towards a predictive cache replacement strategy for multimedia content. J Network Comput Appl, 2013, 36: 219-227 CrossRef Google Scholar

[5] Szabo G, Huberman B A. Predicting the popularity of online content. Commun ACM, 2010, 53: 80--88. Google Scholar

[6] Pinto H, Almeida J M, Gonçalves M A. Using early view patterns to predict the popularity of youtube videos. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining, Rome, 2013. 365--374. Google Scholar

[7] Tsagkias M, Weerkamp W, de Rijke M. Predicting the volume of comments on online news stories. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, Hong Kong, 2009. 1765--1768. Google Scholar

[8] Bandari R, Asur S, Huberman B A. The pulse of news in social media: forecasting popularity. In: Proceedings of the 6th International Conference on Weblogs and Social Media, Dublin, 2012. 26--33. Google Scholar

[9] Kong S B, Mei Q Z, Feng L, et al. Predicting bursts and popularity of hashtags in real-time. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, Gold Coast, 2014. 927--930. Google Scholar

[10] Cui P, Jin S F, Yu L Y, et al. Cascading outbreak prediction in networks: a data-driven approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, 2013. 901--909. Google Scholar

[11] Bao P, Shen H W, Huang J M, et al. Popularity prediction in microblogging network: a case study on sina weibo. In: Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, 2013. 177--178. Google Scholar

[12] Shulman B, Sharma A, Cosley D. Predictability of popularity: gaps between prediction and understanding. In: Proceedings of the 10th International Conference on Web and Social Media, Cologne, 2016. 348--357. Google Scholar

[13] Andersen P K, Borgan Ø, Hjort N L, et al. Counting process models for life history data: a review. Scand J Stat, 1985, 12: 97--158. Google Scholar

[14] Klein J P, Moeschberger M L. Survival Analysis: Techniques for Censored and Truncated Data. Berlin: Springer, 2005. Google Scholar

[15] Shen H W, Wang D S, Song C M, et al. Modeling and predicting popularity dynamics via reinforced Poisson processes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, Québec City, 2014. 291--297. Google Scholar

[16] Gao S, Ma J, Chen Z M. Modeling and predicting retweeting dynamics on microblogging platforms. In: Proceedings of the 8th ACM International Conference on Web Search and Data Mining, Shanghai, 2015. 107--116. Google Scholar

[17] Bao P, Shen H W, Jin X L, et al. Modeling and predicting popularity dynamics of microblogs using self-excited Hawkes processes. In: Proceedings of the 24th International Conference on World Wide Web, Florence, 2015. 9--10. Google Scholar

[18] Zhao Q Y, Erdogdu M A, He H Y, et al. Seismic: a self-exciting point process model for predicting tweet popularity. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, 2015. 1513--1522. Google Scholar

[19] Li C, Ma J Q, Guo X X, et al. Deepcas: an end-to-end predictor of information cascades. In: Proceedings of the 26th International Conference on World Wide Web, Perth, 2017. 577--586. Google Scholar

[20] Cao Q, Shen H W, Cen K T, et al. Deephawkes: bridging the gap between prediction and understanding of information cascades. In: Proceedings of the 26th ACM Conference on Information and Knowledge Management, Singapore, 2017. 1149--1158. Google Scholar

[21] Ouyang S, Li C, Li X. A Peek Into the Future: Predicting the Popularity of Online Videos. IEEE Access, 2016, 4: 3026-3033 CrossRef Google Scholar

[22] Cao Q, Shen H W, Gao H, et al. Predicting the popularity of online content with group-specific models. In: Proceedings of the 26th International Conference on World Wide Web Companion, Perth, 2017. 765--766. Google Scholar

[23] Gao J H, Shen H W, Liu S H, et al. Modeling and predicting retweeting dynamics via a mixture process. In: Proceedings of the 25th International Conference Companion on World Wide Web, Montréal, 2016. 33--34. Google Scholar

[24] Iyengar R, Van den Bulte C, Valente T W. Opinion Leadership and Social Contagion in New Product Diffusion. Marketing Sci, 2011, 30: 195-212 CrossRef Google Scholar

[25] Staab S, Domingos P, Mika P. Social Networks Applied. IEEE Intell Syst, 2005, 20: 80-93 CrossRef Google Scholar

[26] Crane R, Sornette D. Robust dynamic classes revealed by measuring the response function of a social system. Proc Natl Acad Sci USA, 2008, 105: 15649-15653 CrossRef PubMed ADS arXiv Google Scholar

[27] Jeong H, Néda Z, Barabási A L. Measuring preferential attachment in evolving networks. Europhys Lett, 2003, 61: 567-572 CrossRef ADS Google Scholar

[28] Wang M, Yu G, Yu D. Measuring the preferential attachment mechanism in citation networks. Physica A-Statistical Mech its Appl, 2008, 387: 4692-4698 CrossRef ADS Google Scholar

[29] Zeiler M D. Adadelta: an adaptive learning rate method. 2012,. arXiv Google Scholar

qqqq

Contact and support