Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Traditional approaches to community detection, as studied by physicists, sociologists, and
more recently computer scientists, aim at simply partitioning the social network graph …

We present a novel algorithm for significant pattern mining, Westfall-Young light. The target
patterns are statistically significantly enriched in one of two classes of objects. Our method …

In this chapter we describe the use of patterns in the analysis of supervised data. We survey
the different settings for finding patterns as well as sets of patterns. The pattern mining …

The theory of relevance is an approach for redundancy avoidance in labeled itemset mining.
In this paper, we adapt this theory to the setting of sequential patterns. While in the itemset …

Pan Assays Interference Compounds (PAINS) are a significant problem in modern drug
discovery: compounds showing non-target specific activity in high-throughput screening can …

Optimized on parts of data, applied on part of the data (Bringmann and Zimmermann, 2005)
Optimized on all data, applied on all data (Thoma et al, 2009) Optimized on parts of data …

Graph data have been of common practice in many application domains. However, it is very
difficult to deal with graphs due to their intrinsic complex structure. In this paper, we propose …

We present a novel algorithm, Westfall-Young light, for detecting patterns, such as itemsets
and subgraphs, which are statistically significantly enriched in one of two classes. Our …

Methods for detecting community structures in graphs already exist for many years. This
subject is studied by physicists, sociologists and also computer scientists. Traditional …