Abstract: Algorithmic (data-driven) decision making is increasingly being used toassist or replace human decision making in a variety of domains rangingfrom banking (rating user credit) and recruiting (ranking applicants) tojudiciary (profiling criminals) and journalism (recommendingnews-stories). Against this background, in this talk, I will pose andattempt to answer the following high-level questions: (a) Can algorithmic decision making be discriminatory?(b) Can we detect discrimination in decision making?(c) Can we control algorithmic discrimination? i.e., can we make algorithmic decision more fair?

Abstract: Fashion is a very fast moving business addressing many different target groups (style, age, occasion, status, ?). Hence, it is hard to predict what will be fashionable in the future. Originally, how these markets evolved was largely dictated by a relatively small set of players, such as designers, brands or celebrities. Nowadays, social media is to some extend changing the rules of play. Bloggers and other participants in social media are increasingly playing an important role in defining and spreading fashion trends. Zalando wants to take active part in this development. Some of our goals are to discover new influencers in the fashion world, match influencers to specific brands or advertising campaigns and to discover and to monitor emerging trends present in social media. To do this, we have to construct a large network of entities (such as bloggers, brands, magazines) to be able to analyze the dynamic behavior of the fashion world and answer the questions mentioned above. For this talk, we studied a very popular fashion platform with around 1M subscribers and 22M connections. We will address questions such as which geographical regions of the world are most active or if people tend to follow influential users from their same country or region. In addition, we will analyze if some of the standard properties of complex networks apply in our example, such as small world, scale free, etc.

Abstract: Spatially embedded complex networks, such as nervous systems, the Internet, and transportation networks, generally have nontrivial topological patterns of connections combined with nearly minimal wiring costs. We report here the empirical analysis of two databases describing respectively: 200 years of evolution of the road network in a large area located north of Milan (Italy), and the growth of the nervous system of the C. elegans from the moment of fertilization to adulthood. We discuss the basic mechanisms that drive the evolution of such two spatial networks.

Abstract: The structure and dynamics of interactions is crucial for many phenomena of interest, like contacts between individuals, data transfers, commercial exchanges, mobility, and many others. Analyzing such interactions classicaly relies on network analysis, which captures the structure of interactions, or on temporal series, which captures their dynamics. Both approaches have been extended in various ways to cope with the both structural and temporal nature of interactions, but current situation remains unsatisfactory. I will present here the modeling of interactions over time by stream graphs and link streams, which aims at unifying both aspects into a simple, efficient and intuitive way. It provides a language to deal with interactions over time, in a waysimilar to the language provided by network science for relations.

Abstract: Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication. Across the world, millions of users interact with search engines every day to satisfy their information needs. As the Web grows bigger over time, such information needs, manifested through user search queries, also become more complex. However, there has been no systematic study that quantifies the structural complexity of Web search queries. In this research, we make an attempt towards understanding and characterizing the syntactic complexity of search queries using a multi-pronged approach. We use traditional statistical language modeling techniques to quantify and compare the perplexity of queries with natural language (NL). We then use complex network analysis for a comparative analysis of the topological properties of queries issued by real Web users and those generated by statistical models. Finally, we conduct experiments to study whether search engine users are able to identify real queries, when presented along with model-generated ones. The three complementary studies show that the syntactic structure of Web queries is more complex than what n-grams can capture, but simpler than NL. Queries, thus, seem to represent an intermediate stage between syntactic and non-syntactic communication.