Modeling of large-scale social network services based on mechanisms of information diffusion: Sina Weibo as a case study

The purpose of this paper is to investigate the characteristics of the dissemination of information in the community. A variety of possible factors that affect the dissemination of information in Sina Weibo have been discussed. By analyzing the process of the information dissemination in the community of Sina Weibo, we found the information dissemination of Weibo community and the dynamic model are very similar. With the aid of data intensive computing theory, the various features have been mined and modeled. The dynamic model is improved and redefined to characterize the community. Then the SEINR model is proposed. The basic reproductive number, the existence of equilibrium point and the stability of the network are analyzed and proved in detail. By comparing with real data in Weibo community, we show that the SEINR model accurately reflects the dissemination of information community. Furthermore, we investigate the SEINR model in detail to show the influences of different parameters on information dissemination by simulations. The SEINR model to study the dissemination of information is proposed.The model relies on data intensive computing theory and the epidemic model.The basic reproductive number and the equilibrium point are crucial.Simulation results reflect the real information dissemination process.

[1]  Chen Wang,et al.  Finding Influentials Based on the User's Behavior in Microblogging , 2013 .

[2]  Michael Small,et al.  Exactly scale-free scale-free networks , 2013, ArXiv.

[3]  Michael S. Bernstein,et al.  Quantifying the invisible audience in social networks , 2013, CHI.

[4]  Sun-Yuan Kung,et al.  Profit Improvement in Wireless Video Broadcasting System: A Marginal Principle Approach , 2015, IEEE Transactions on Mobile Computing.

[5]  Stefan Stieglitz,et al.  Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior , 2013, J. Manag. Inf. Syst..

[6]  Ewan Klein,et al.  A semantic web of know-how: linked data for community-centric tasks , 2014, WWW '14 Companion.

[7]  Juha Heinanen,et al.  OF DATA INTENSIVE APPLICATIONS , 1986 .

[8]  Ben Y. Zhao,et al.  Understanding latent interactions in online social networks , 2010, IMC '10.

[9]  Jhing-Fa Wang,et al.  A Novel Video Summarization Based on Mining the Story-Structure and Semantic Relations Among Concept Entities , 2009, IEEE Transactions on Multimedia.

[10]  Ru Wang,et al.  Survey on Sina Weibo Research Based on Big Data Mining , 2015 .

[11]  Ya Zhang,et al.  Learning the Hotness of Information Diffusions with Multi-dimensional Hawkes Processes , 2013, ADMI.

[12]  Jae-Gil Lee,et al.  Geospatial Big Data: Challenges and Opportunities , 2015, Big Data Res..

[13]  Jhing-Fa Wang,et al.  Smart Homecare Surveillance System: Behavior Identification Based on State-Transition Support Vector Machines and Sound Directivity Pattern Analysis , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  George Pallis,et al.  On the Impact of Online Social Networks in Content Delivery , 2014 .

[15]  Jure Leskovec,et al.  The bursty dynamics of the Twitter information network , 2014, WWW.

[16]  F. Richard Yu,et al.  A Mean Field Game Theoretic Approach for Security Enhancements in Mobile Ad hoc Networks , 2014, IEEE Transactions on Wireless Communications.

[17]  Jussara M. Almeida,et al.  Using early view patterns to predict the popularity of youtube videos , 2013, WSDM.

[18]  Christopher P. Monterola,et al.  A Dynamical Model of Twitter Activity Profiles , 2015, HT.

[19]  Katarzyna Musial,et al.  Social networks on the Internet , 2012, World Wide Web.

[20]  Aristides Gionis,et al.  Discovering Dynamic Communities in Interaction Networks , 2014, ECML/PKDD.

[21]  Przemyslaw Kazienko,et al.  GED: the method for group evolution discovery in social networks , 2012, Social Network Analysis and Mining.

[22]  Lan Chen,et al.  Semantic Link Network-Based Model for Organizing Multimedia Big Data , 2014, IEEE Transactions on Emerging Topics in Computing.

[23]  Avery E. Holton,et al.  Seeking and Sharing: Motivations for Linking on Twitter , 2014 .

[24]  Jawwad Shamsi,et al.  Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions , 2013, Journal of Grid Computing.

[25]  Piet Van Mieghem,et al.  Generalized Epidemic Mean-Field Model for Spreading Processes Over Multilayer Complex Networks , 2013, IEEE/ACM Transactions on Networking.

[26]  Saurabh Goyal,et al.  Like-Minded Communities: Bringing the Familiarity and Similarity together , 2012, WISE.

[27]  Christopher J. Hughes,et al.  Performance evaluation of Intel® Transactional Synchronization Extensions for high-performance computing , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[28]  Michalis Vazirgiannis,et al.  Clustering and Community Detection in Directed Networks: A Survey , 2013, ArXiv.

[29]  Cécile Favre,et al.  Information diffusion in online social networks: a survey , 2013, SGMD.

[30]  Xiaomei Quan,et al.  Survey: Functional Module Detection from Protein-Protein Interaction Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[31]  Wandong Cai,et al.  A sequential game-theoretic study of the retweeting behavior in Sina Weibo , 2015, The Journal of Supercomputing.

[32]  Cliff Lampe,et al.  Cultivating Social Resources on Social Network Sites: Facebook Relationship Maintenance Behaviors and Their Role in Social Capital Processes , 2014, J. Comput. Mediat. Commun..

[33]  Jorge Bernardino,et al.  Scalability of Facebook Architecture , 2015, WorldCIST.

[34]  Boleslaw K. Szymanski,et al.  Overlapping community detection in networks: The state-of-the-art and comparative study , 2011, CSUR.

[35]  Steve Harenberg,et al.  Community detection in large‐scale networks: a survey and empirical evaluation , 2014 .

[36]  Yifan Hu,et al.  A Maxent-Stress Model for Graph Layout , 2012, IEEE Transactions on Visualization and Computer Graphics.

[37]  C. Nazé,et al.  ris3: A program for relativistic isotope shift calculations , 1997, Comput. Phys. Commun..

[38]  John Klein,et al.  Runtime Performance Challenges in Big Data Systems , 2015, WOSP '15.

[39]  Yiqiang Chen,et al.  Profit Optimization for Wireless Video Broadcasting Systems Based on Polymatroidal Analysis , 2015, IEEE Transactions on Multimedia.

[40]  C. L. Philip Chen,et al.  Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..

[41]  Vangalur S. Alagar,et al.  Publishing and discovering context-dependent services , 2013, Human-centric Computing and Information Sciences.

[42]  D. K. Lobiyal,et al.  Performance evaluation of data aggregation for cluster-based wireless sensor network , 2013, Human-centric Computing and Information Sciences.

[43]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.