How does Shakespeare compare against modern rap artists?

A couple of months ago Eric Malmi wrote about his work on Raplyzer, a method for analyzing Finnish rap lyrics. With the use of a speech synthesizer, Eric has now extended the method to English rap lyrics. Using the new version of the analyzer, he ranked 94 rap artists based on their rhyme factor, and even threw Shakespeare in the mix. He describes the results in a new blog post.


Additionally, if you are looking for more action, you may want to battle rap against BattleBot.

Overlapping community detection in labeled graphs

In a recent paper with Esther Galbrun and Nikolaj Tatti, presented in the journal of Data Mining and Knowledge Discovery, we worked on the problem of discovering overlapping communities in networks with labeled vertices. The model is motivated by social networks, where vertex labels are used to represent information about individuals, such as occupation, hobbies, preferences, etc. The hypothesis is that the vertex labels can be used to derive and explain the community structure in the network. Continue reading

How graph algorithms can help to find interesting events

In our recent KDD paper, with Polina Rozenshtein, Aris Anagnostopoulos, and Nikolaj Tatti, we worked on the problem of finding events in graphs. We abstracted the event-finding problem with the following simple formulation: Given a graph with node weights and edge distances, find a subset of nodes (the event) that have large sum of weights and are well connected. In the paper we showed how to use this formulation to find interesting events in real-world datasets. Continue reading

The network response problem

Understanding the mechanisms of influence and contagion in human interactions is a central focal point in today’s research, in computational social science, machine learning, and data mining. Quantifying the effects of social influence was the question behind the controversial recently-published facebook experiment, which aimed to show that emotional contagion occurs on social networks (the NYT’s article).

Continue reading