Helsinki Institute for Information Technology

About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.

Discovering dynamic communities in interaction networks

Abstract: Online social networks are often defined by considering interactions over large time intervals, e.g., consider pairs of individuals who have called each other at least once in a mobilie-operator network, or users who have made a conversation in a social-media site. Although such a definition can be valuable in many graph-mining tasks, it suffers from a severe limitation: it neglects the precise time that the interaction between network nodes occurs. In this paper we study interaction networks, where one considers not only the social-network topology, but also the exact time that nodes interact. In an interaction network an edge is associated with a time stamp, and multiple edges may occur for the same pair of nodes. Consequently, interaction networks offer a more fine-grained representation that can be used to reveal otherwise hidden dynamic phenomena in the network. We consider the problem of discovering communities in interaction networks, which are dense and whose edges occur in short time intervals. Such communities represent groups of individuals who interact with each other in some specific time instances, for example, a group of employees who work on a project and whose interaction intensifies before certain project milestones.We prove that the problem we define is NP-hard, and we provide effective algorithms by adapting techniques used to find dense subgraphs. We perform extensive evaluation of the proposed methods on synthetic and real datasets, which demonstrates the validity of our concepts and the good performance of our algorithms.

Comparing type counts: The case of women, men and -ity in early English letters

Abstract: This work is a case study of applying nonparametric statistical methods to corpus data. We show how to use ideas from permutation testing to answer linguistic questions related to morphological productivity and type richness. In particular, we study the use of the suffixes -ity and -ness in the 17th-century part of the Corpus of Early English Correspondence within the framework of historical sociolinguistics. Our hypothesis is that the productivity of -ity, as measured by type counts, is significantly low in letters written by women. To test such hypotheses, and to facilitate exploratory data analysis, we take the approach of computing accumulation curves for types and hapax legomena. We have developed an open source computer program which uses Monte Carlo sampling to compute the upper and lower bounds of these curves for one or more levels of statistical significance. By comparing the type accumulation from women’s letters with the bounds, we are able to confirm our hypothesis.

Inducing involuntary musical imagery: An experimental study

Abstract: It is still a mystery why we sometimes experience the repetition of memories in our minds. This phenomenon seems to be particularly prominent in music. We believe that present lack of knowledge relates to the lack of methods available for the study of this topic. To improve the understanding of involuntary musical imagery (INMI), this paper proposes a novel method to induce it in experimental settings. We report three experiments that were conducted to evaluate two research questions related to INMI: Can it be experimentally induced, and if so, which factors influence its emergence? Investigation particularly focused on how recent activation of musical memory might predict INMI. The questions were tested in single-trial experiments conducted over the internet. The experiments utilized a cued-recall method to induce INMI and delayed self-reports. Among a large sample of people, the prevalence of the phenomenon was considerable. When the familiarity with the stimuli was controlled for, inducing INMI experim...

Learning Bounded Tree-width Bayesian Networks using Integer Linear Programming

Abstract: In many applications one wants to compute conditional probabilities given a Bayesian network. This inference problem is NP-hard in general but becomes tractable when the network has low tree-width. Since the inference problem is common in many application areas, we provide a practical algorithm for learning bounded tree-width Bayesian networks. We cast this problem as an integer linear program (ILP). The program can be solved by an anytime algorithm which provides upper bounds to assess the quality of the found solutions. A key component of our program is a novel integer linear formulation for bounding tree-width of a graph. Our tests clearly indicate that our approach works in practice, as our implementation was able to nd an optimal or nearly optimal network for most of the data sets.