Real-life Social Networks Research Articles

Information Distribution Analysis Based on Human's Behavior State Model and the Small-World Network

In this paper, an information distribution model based on human's behavior is proposed. We also propose dynamic parameters to make the model more practical for real life social network. Subsequently, the simulations are conducted based on the small-world network and its characteristics, and the parameters in the model are analyzed to increase efficiently the power of information distribution. Our study and simulation results show that the proposed model can be used to analyze and predict the effectiveness of information distribution. Moreover, the study also shows how to use the model parameters to increase power of the distribution.

Antecedents and Consequences of Online Social Networking Behavior: The Case of Facebook

ABSTRACT Despite the recent popularity of online social networks, there are few available studies that explain the differences between real life and internet social networks. Authoritative information about the outcomes of using social networking websites is even more sparse. In an attempt to close this literature gap, this exploratory study found that online social networks and real life social networks are significantly different in terms of social network size. The results also show that gender and extroversion are the major predictors of both online social network size and time spent online for social networking. Perhaps the most interesting finding is the negative impact of self-esteem on inclusion of strangers in online social networks. Additionally, directions for future research are provided.

In Search of Stars: Network Formation among Heterogeneous Agents

This paper reports results from a laboratory experiment on network formation among heterogeneous agents. The experimental design extends the Bala-Goyal (2000) model of network formation with decay and two-way flow of benefits by allowing for agents with lower linking costs or higher benefits to others. Furthermore, agents’ types may be common knowledge or private information. In all treatments, the (efficient) equilibrium network has a “star” structure. With homogeneous agents, equilibrium predictions fail completely. In contrast, with heterogeneous agents stars frequently occur, often with the high-value or low-cost agent in the center. Stars are not born but rather develop: with a high-value agent, the network’s centrality, stability, and efficiency all increase over time. Probit estimations based on best-response behaviour and other-regarding preferences are used to analyze individual linking behavior. Our results suggest that heterogeneity is a major determinant for the predominance of star-like structures in real-life social networks.

Overview of the 2003 KDD Cup

This paper surveys the 2003 KDD Cup, a competition held in conjunction with the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) in August 2003. The competition focused on mining the complex real-life social network inherent in the e-print arXiv (arXiv.org). We describe the four KDD Cup tasks: citation prediction, download prediction, data cleaning, and an open task.