PhD proposal title:

Analysis and study of the traffic demand in social networks. Evaluation and evolution of the traffic demand on several social network scenarios.

PhD supervisors:

Prof. José-Luis Melús and Dr. Marcos Postigo

In the aim to understand and to determine what are the necessary resources that network or service operators need to offer their services it is useful to know the user traffic patterns generated in its access to these services. This traffic not only depends on the type of device that the user utilizes, but how it uses. In fact the used technology is really important but the habits of the users when use these services could be crucial.  More often these habits are close related with the relationships with their friends.  In this sense social networks are fundamental in achieving this goal. Millions of people in the world are connected every day through this massive communication strategy. Personal, familiar or professional relations of people are influenced by the use of social networks.  This work analyzes and evaluates the influence that social networks have in the traffic that circulates by networks and, in this sense, it try to establish the type of this traffic and to quantify its associated demand. It is not quite difficult to admit that the topology of the network and the typology of its use will play an important role in the determination of the present traffic and, of course, it could be an approximation to evaluate its evolution in the future. Thus, to know how the social network is, where users are involved in or, to determine the connectivity degree that users have in each network they utilize, could be important factors in the determination of the type of influence that each user exercises on others and vice versa and, of course, in the demand of traffic that they require.

Objectives:

• To analyze the state of the art in the field of proposed demand models or patterns of traffic for the traffic generated in the present networks (wireless networks and users with smartphones, tablets, etc.).
• To define appropriate models for traffic generation (based on agents that are able to simulate the behavior of individual users) that allows simulating the demand of the offered services.
• To analyze the influence of different typologies of social networks on the generation of distinct types of traffic. The typology of each network could be classified according to some metrics such as its connectivity (homophily, multiplexity, mutuality, etc.), its distribution (bridge, centrality, density, distance, etc.) and its segmentation (clustering coefficient, cohesion, etc.).
• To define a model to generate traffic (volume and characterization) based on the social network which generates this traffic. This model should tackle the evolution of the demand on by means of data clustering techniques, temporal smoothing-driven evolving clustering, milestone-driven-evolving clustering or incremental adaptation-driven-evolving clustering).

Previous Know-How or backgroud required:

It is recommended a basic knowledge in these areas:

• Existent patterns analysis applied to demand in network traffic
• Graph theory and metrics in network topologies with applications to social networks.
• Simulation techniques or special ad- hoc software appropriated to the generation of network topologies.
• Analytical techniques that allow to evaluate and to compare different network topology scenarios in social networks

Methodology

The suggested steps to develop this work are:

• Analysis of the state of the art in the aim to eliminate ways that have been followed by other authors. In parallel the scenarios (in social networks) suggested in this work should be defined.
• Proposal and implementation of the selected scenarios using the available simulation techniques or creating new ones (ad-hoc).
• Evaluation and comparison by simulation or analytical techniques, based on different metrics used in networks and in the selected scenarios, of the type and characteristics of the traffic generated in different social networks.

References:

• Stanley Wasserman and Katherine Faust, “Social Network Analysis: Methods and Analysis”, Cambridge University Press, Cambridge, UK, 1999 reprint (1994 original).
• Easley D., Kleinberg J., “Networks, Crowds and Markets“, Cambridge University Press, 2010
• Jackson M.O. “Social and Economic Networks”, Princeton University Press, 2008.
• Newman M.E. J. “Networks. An Introduction”,  Oxford University Press, 2010.
• Y. Jin, et al. “Characterizing data usage patterns in a large cellular network”. In Proceedings of the 2012 ACM SIGCOMM workshop on Cellular networks: operations, challenges, and future design (CellNet '12), ACM, New York, NY, USA, pp. 7-12, 2012.
• J. Kang J., S. Seo S., H. J.W.-K., "Usage pattern analysis of smartphones", Network Operations and Management Symposium (APNOMS), 2011 13th Asia-Pacific, vol.1, nº 8, pp: 21-23 Sept. 2011.
• Giatsoglou M., Vakali A., "Capturing Social Data Evolution Using Graph Clustering",  IEEE Internet Computing, vol.17, nº.1, pp.74,79, Jan.-Feb. 2013.
• Olfati-Saber R., "Evolutionary dynamics of behavior in social networks", 46th IEEE Conference on Decision and Control, 2007, pp: 4051-4056, Dec. 2007.
• Singh B, Gupte N. (2005), “Congestion and decongestion in a communication network”, Phys. Rev. E 71(5):055103.
• Krebs V., "The Social Life of Routers", Internet Protocol Journal 3, pp. 14-25, 2000.
• Kas, M., et al., "What if wireless routers were social? Approaching wireless mesh networks from a social networks perspective, "IEEE Wireless Communications, vol.19, nº6, pp: 36,43, December 2012