CSSS 2010 Santa Fe-Final Papers
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| CSSS Santa Fe 2010 |
Human Mobility in an Online World
Massive multiplayer online games provide a fascinating new way of observing hundreds of thousands of simultaneously interacting individuals engaged in virtual socio-economic activities. We have compiled a data set consisting of practically all actions of all players over a period of three years from an online game played by over 350,000 people. The universe of this online world is a network on which players move to interact with other players. This interaction may consist of trade, armed conflict, friendship and enmity. We focus on the mobility of human players on the network over a time-period of 500 days. We take a number of mobility measurements (daily and biweekly position changes, entropy, number of unique nodes visited) of players and compare them with measures of simulated random walkers on the same topology. Player mobility is highly different from the mobility of unbiased random walkers. The analysis of biased random walkers reveals the two essential ingredients which explain measured human mobility patterns most accurately: heterogeneity and a tendency to return to recently visited locations. We compare our entropy distributions with human mobility in real life world -- measured via mobile phone data -- and find a striking match.
By Michael Szell, Giovanni Petri, Kang Zhao, Drew Levin
Who Blogs What: Understanding Behavior, Impact and Types of Bloggers
We investigated bloggers’ publishing patterns by focusing on the topics that their posts cover. Applying clustering algorithms on the dataset from a blog website of 370,000 posts from 2,275 blogs, we identified two types of bloggers: specialists and generalists. Then we compared their respective contributions to the blogosphere in terms of productivity and buzz-factor. Our analysis suggests that specialists generally have a higher impact than generalists. It also reveals that among specialists, there are very few who create a large “buzz” or produce a voluminous output.
By Kang Zhao and Massimiliano Spaziani
Local and nonlocal information in a traffic network: how important is the horizon?
Recent advances in distributed sensor network technology have changed the landscape of traffic optimization in which small, mobile devices are able to sense local information and communicate in real time with one another. Naive optimization algorithms that operate solely on the local or global level are inherently flawed, as global optimization requires every local sensor to communicate with a centralized base-station, creating prohibitive bandwidth, robustness, and security concerns, while local optimization methods are limited by a near information horizon as they are unable to propagate or react to information beyond their immediate vicinity. This paper investigates an intermediate approach where individual sensors are able to propagate congestion information over a variable distance that is determined in real-time. This strategy consistently out-performs a naive strategy where every car simply takes the shortest path to its destination, but does worse than a simpler optimization algorithm that only incorporates local information. This is most likely because the intermediate solution directs cars along the same alternate path when attempting to free a congested area, thus creating new congestion along the detour. The results suggest that local information might set an upper bound on performance in models of cascading information. Further work is required to confirm this observation and develop an algorithm able to join both local and global information to effectively diffuse traffic around congestion.
By Giovanni Petri, Samuel Scarpino, Drew Levin, Tracey McDole, Kang Zhao and Leif Karlstrom