Budak C, Agrawal D, El Abbadi A. Diffusion of Information in Social Networks: Is It All Local?[C]//Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 2012: 121-130.
将模型分为了Local和Global两类，在Global模型中引入了diffusion of innovation相关研究的思想。
Matsubara Y, Sakurai Y, Prakash B A, et al. Rise and fall patterns of information diffusion: model and implications[C]//Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012: 6-14.
结合SI与Self-excited Hawkes process，考虑节点活动的周期性，得出以时间为自变量的函数，衡量一个hashtag或者meme整体传播情况，可做预测
复杂网络方面，参见《复杂网络及其应用》汪小帆等编著：
Zanette D H. Dynamics of rumor propagation on small-world networks[J]. Physical review E, 2002, 65(4): 041908.
谣言在小世界网络中的传播，SIR
Moreno Y, Nekovee M, Pacheco A F. Dynamics of rumor spreading in complex networks[J]. Physical Review E, 2004, 69(6): 066130.
发展了Daley等人1964年提出的谣言传播模型，状态分成Ignorants、Spreaders，Stiflers三种类型
Dodds P S, Watts D J. Universal behavior in a generalized model of contagion[J]. Physical review letters, 2004, 92(21): 218701.
传播蔓延现象的推广模型与普遍行为。作者认为所有的传播蔓延现象可归结成两类，一类是泊松模型，一类是临界值模型。前者连续的接触所导致的传播独立与概率p,后者超过某一临界值后传播带来的感染概率迅速增大。
[Hethcote 2000] H. W. Hethcote. The Mathematics of Infectious Diseases. SIAM Review. 2000, 42: 599-653.
[Kumar2006] R. Kumar, J. Novak, A. Tomkins. Structure and Evolution of Online Social Networks. Proc. of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006.
[Doucet2000] A.Doucet, N.de Freitas and K.Murphy. Rao-blackwellished Particle Filtering for Dynamic Bayesian Networks. Proc. of the 16th Conference on Uncertainty in Artificial Intelligence. 2000.
[Kask2001] K. Kask, R. Dechter and J. Larrosal, et al. Buchet-tree Elimination for Automated Reasoning. Artificial Intelligence. 2001,(125): 91-131
[Murphy2001]K. Murphy and Y. Weiss. The Factored Frontier Algorithm for Approximate Inference in DBNs. Computer Science Department University of California Berkeley. CA , 2001
[Paskin2003] M.A. Paskin. Thin Junction Tree Filters for Simultaneous Localization and Mapping. Proc. of the 18th International Joint Conference on Artificial Intelligence. 2003.
[Kempe2003] D. Kempe, J. Kleinberg and E. Tardos. Maximizing the Spread of Influence through a Social Network. Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
Ho C T, Li C T, Lin S D. Modeling and visualizing information propagation in a micro-blogging platform[C]//Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on. IEEE, 2011: 328-335.
Stanford SNAP组的研究：
Leskovec J, Backstrom L, Kleinberg J. Meme-tracking and the dynamics of the news cycle[C]//Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009: 497-506.
Yang J, Leskovec J. Modeling information diffusion in implicit networks[C]//Data Mining (ICDM), 2010 IEEE 10th International Conference on. IEEE, 2010: 599-608.
LIM模型
Gomez Rodriguez M, Leskovec J, Krause A. Inferring networks of diffusion and influence[C]//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: 1019-1028.
Yang J, Leskovec J. Patterns of temporal variation in online media[C]//Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011: 177-186.
对传播曲线聚类，得出六种模式
Myers S A, Zhu C, Leskovec J. Information diffusion and external influence in networks[C]//Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012: 33-41.
考虑外部影响
Myers S A, Leskovec J. Clash of the contagions: Cooperation and competition in information diffusion[C]//Data Mining (ICDM), 2012 IEEE 12th International Conference on. IEEE, 2012: 539-548.
考虑信息的碰撞、竞争
Gomez Rodriguez M, Leskovec J, Schölkopf B. Structure and dynamics of information pathways in online media[C]//Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 2013: 23-32.
首先需要粗略汇总资料。