We are proud to welcome two visitors in our department. Prof. Santo Fortunato, full professor at Indiana University, is one of the most important network scientist, whose influential contrubutions on community detection changed our perspective on network analysis. Ann Samoilenko, PhD Student at Gesis, is a rising star in the field of computational social science and digital humanities.
Their seminars are scheduled on October 13th respectively at 14:00 and 15:00, to be delivered at Sala Riunioni, first floor at Department of Computer Science, University of Turin. Please, find below titles and abstracts of the talks.
14:00 – Community structure in complex networks, Prof. Santo Fortunato
Complex systems typically display a modular structure, as modules are easier to assemble than the individual units of the system, and more resilient to failures. In the network representation of complex systems, modules, or communities, appear as subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network. In this talk I will address three fundamental questions: How is community structure generated? How to detect it? How to test the performance of community detection algorithms? I will show that communities emerge naturally in growing network models favoring triadic closure, a mechanism necessary to implement for the generation of large classes of systems, like e.g. social networks. I will discuss the limits of the most popular class of clustering algorithms, those based on the optimization of a global quality function, like modularity maximization. Testing algorithms is probably the single most important issue of network community detection, as it implicitly involves the concept of community, which is still controversial. I will discuss the importance of using realistic
benchmark graphs with built-in community structure, as well as the role of metadata.
15:00 – Learning about history from multilingual Wikipedia: How are country histories presented across the language editions?, Ann Samoilenko
Each description of history inherently presents a unique viewpoint on past events, and it might be partial and disputable. Quantifying such differences is a challenging task. In this work, I introduce an approach that automatically identifies such differences by computing timelines and detecting temporal focal points of written history of nation states. My colleagues and I compare the descriptions of national histories of 193 UN member states in 30 large Wikipedia language editions, by computing their timelines over the last 1,000 years, and detecting the periods of highest importance for each of the countries. We show quantitatively that Wikipedia narratives about national histories (i) are skewed towards more recent events; (ii) are distributed unevenly across the continents with significant focus on the history of European countries; and (iii) vary across language editions, although average interlingual consensus is rather high.