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Monday, 4 June 2018

Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction

Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction

ABSTRACT:
How to model the process of information diffusion in social networks is a critical research task. Although numerous attempts have been made for this study, few of them can simulate and predict the temporal dynamics of the diffusion process. To address this problem, we propose a novel information diffusion model (GT model), which considers the users in network as intelligent agents. The agent jointly considers all his interacting neighbors and calculates the payoffs for his different choices to make strategic decision. We introduce the time factor into the user payoff, enabling the GT model to not only predict the behavior of a user but also to predict when he will perform the behavior. Both the global influence and social influence are explored in the time dependent payoff calculation, where a new social influence representation method is designed to fully capture the temporal dynamic properties of social influence between users. Experimental results on Sina Weibo and Flickr validate the effectiveness of our methods.
EXISTING SYSTEM:
  • In “Scalable influence maximization for prevalent viral marketing in large-scale social networks”: Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks.
  • Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads.
  • In existing system, the authors design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. The system has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm.
DISADVANTAGES OF EXISTING SYSTEM:
  • How to model the process of information diffusion in social networks is a critical research task.
  • Although numerous attempts have been made for this study, few of them can simulate and predict the temporal dynamics of the diffusion process.
PROPOSED SYSTEM:
  • In this paper, we propose a novel information diffusion model (GT model) for temporal dynamic prediction. In contrast to traditional theory-centric models, the GT model regards the users in the network as intelligent agents. It can capture both the behavior of individual agent and the strategic interactions among these agents. By introducing the time-dependent payoffs, the GT model is able to predict the temporal dynamics of the information diffusion process. Different from most data-centric models, the GT model can not only predict whether a user will perform a behavior but also can predict when he will perform it.
  • In the proposed GT model, the diffusion process unfolds in discrete time-steps t, and begins from a given initial active user set. When a user observes a piece of information at time t, he calculates his payoffs for different choices depending on his neighbors’ status so as to make strategic decision.
ADVANTAGES OF PROPOSED SYSTEM:
  • We propose a novel information diffusion model (GT model), where, between different choices (behaviors), the user jointly considers all his interacting neighbors’ choices to make strategic decisions that maximizes his payoff.
  • We propose a time-dependent user payoff calculation method in the GT model by exploring both the global influence and social influence.
  • We propose a new social influence representation method, which can accurately capture the temporal dynamic properties of social influence between users.
  • We conduct experiments on datasets. The comparison results with closely related work indicate the superiority of the proposed GT model.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS: 
  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB
SOFTWARE REQUIREMENTS: 
  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool :         Netbeans 7.2.1
  • Database : MYSQL
REFERENCE:
Dong Li, Zhiming Xu, Yishu Luo, Sheng Li, Anika Gupta_Katia Sycara, Shengmei Luo, Lei Hu, Hong Chen, “Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Project Cost :  10000

Thanks and Regards,
Mumbai Academics | Airoli 
8097636691 (Gaurav Sir)[Project Manager]
7506234650 (Hema Yadav)[HR]
Row House No 7,Opp Datta Meghe College, 
Sector 2,Airoli ,Navi Mumbai MH 400708

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