On Using Temporal Networks to Analyze User Preferences Dynamics

User preferences are fairly dynamic, since users tend to exploit a wide range of information and modify their tastes accordingly over time. Existing models and formulations are too constrained to capture the complexity of this underlying phenomenon. In this paper, we investigate the interplay between user preferences and social networks over time. We propose to analyze user preferences dynamics with his/her social network modeled as a temporal network. First, we define a temporal preference model for reasoning with preferences. Then, we use evolving centralities from temporal networks to link with preferences dynamics. Our results indicate that modeling Twitter as a temporal network is more appropriated for analyzing user preferences dynamics than using just snapshots of static network.

[1]  Fenrong Liu,et al.  Preference Change and Information Processing , 2006 .

[2]  Fenrong Liu,et al.  Reasoning about Preference Dynamics , 2011 .

[3]  João Gama,et al.  Evolving Centralities in Temporal Graphs: A Twitter Network Analysis , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).

[4]  Cecilia Mascolo,et al.  Temporal distance metrics for social network analysis , 2009, WOSN '09.

[5]  Charu C. Aggarwal,et al.  Evolutionary Network Analysis , 2014, ACM Comput. Surv..

[6]  Hakim Hacid,et al.  A predictive model for the temporal dynamics of information diffusion in online social networks , 2012, WWW.

[7]  Cecilia Mascolo,et al.  Graph Metrics for Temporal Networks , 2013, ArXiv.

[8]  Alexandros Nanopoulos,et al.  Modeling the dynamics of user preferences in coupled tensor factorization , 2014, RecSys '14.

[9]  Reza Zafarani,et al.  Social Media Mining: An Introduction , 2014 .

[10]  Jimeng Sun,et al.  Temporal recommendation on graphs via long- and short-term preference fusion , 2010, KDD.

[11]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[12]  Hideyuki Imai,et al.  Community Change Detection in Dynamic Networks in Noisy Environment , 2015, WWW.

[13]  Jaideep Srivastava,et al.  Measuring spontaneous devaluations in user preferences , 2013, KDD.

[14]  Fabiola S. F. Pereira,et al.  Mining comparative sentences from social media text , 2015 .

[15]  F. Liu,et al.  Preference Change: A Quantitative Approach∗ , 2009 .

[16]  Ben Y. Zhao,et al.  Process-driven Analysis of Dynamics in Online Social Interactions , 2015, COSN.

[17]  Petter Holme,et al.  Analyzing Temporal Networks in Social Media , 2014, Proceedings of the IEEE.

[18]  Argimiro Arratia,et al.  Forecasting with twitter data , 2013, ACM Trans. Intell. Syst. Technol..

[19]  Cecilia Mascolo,et al.  Analysing information flows and key mediators through temporal centrality metrics , 2010, SNS '10.

[20]  Kathleen M. Carley,et al.  Measuring Temporal Patterns in Dynamic Social Networks , 2015, ACM Trans. Knowl. Discov. Data.

[21]  Peter C. Ordeshook,et al.  Endogenous time preferences in social networks , 2005 .

[22]  Yi Lu,et al.  Path Problems in Temporal Graphs , 2014, Proc. VLDB Endow..

[23]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[24]  Daniel Jurafsky,et al.  Inferring User Preferences by Probabilistic Logical Reasoning over Social Networks , 2014, ArXiv.

[25]  Mohammad Ali Abbasi,et al.  Scalable learning of users' preferences using networked data , 2014, HT.