Predicting trends in social networks via dynamic activeness model

With the effect of word-of-the-mouth, trends in social networks are now playing a significant role in shaping people's lives. Predicting dynamic trends is an important problem with many useful applications. There are three dynamic characteristics of a trend that should be captured by a trend model: intensity, coverage and duration. However, existing approaches on the information diffusion are not capable of capturing these three characteristics. In this paper, we study the problem of predicting dynamic trends in social networks. We first define related concepts to quantify the dynamic characteristics of trends in social networks, and formalize the problem of trend prediction. We then propose a Dynamic Activeness (DA) model based on the novel concept of activeness, and design a trend prediction algorithm using the DA model. We examine the prediction algorithm on the DBLP network, and show that it is more accurate than state-of-the-art approaches.

[1]  Jure Leskovec,et al.  Information diffusion and external influence in networks , 2012, KDD.

[2]  Masahiro Kimura,et al.  Prediction of Information Diffusion Probabilities for Independent Cascade Model , 2008, KES.

[3]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[4]  Mudhakar Srivatsa,et al.  Microscopic Social Influence , 2012, SDM.

[5]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[6]  J. Leskovec,et al.  Cascading Behavior in Large Blog Graphs Patterns and a model , 2006 .

[7]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[8]  Cosma Rohilla Shalizi,et al.  Homophily and Contagion Are Generically Confounded in Observational Social Network Studies , 2010, Sociological methods & research.

[9]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[10]  Jon M. Kleinberg,et al.  The structure of information pathways in a social communication network , 2008, KDD.

[11]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[12]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[13]  Masahiro Kimura,et al.  Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis , 2009, ACML.

[14]  M. Zhao,et al.  On maximum likelihood estimation for a general non-homogeneous Poisson process , 1996 .

[15]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, WWW '04.

[16]  Jorge Lobo,et al.  Learning Stochastic Models of Information Flow , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[17]  Jure Leskovec,et al.  Meme-tracking and the dynamics of the news cycle , 2009, KDD.

[18]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[19]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[20]  Divyakant Agrawal,et al.  Structural Trend Analysis for Online Social Networks , 2011, Proc. VLDB Endow..

[21]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..