Descriptive Modeling of Social Networks

AbstractThese last years, many analysis methods have been proposed to extract knowledge from social networks. As for the traditional data mining domain, these network-based approaches can be classified according to two main families. The approaches based on predictive modelling, which encompass the techniques that analyse current and historical facts to make predictive assumptions about future or unknown events. The approaches based on descriptive modelling, which cover the set of techniques that aim to summarize the data by identifying some relevant features in order to describe how things organize and actually work. In this paper, we review the main descriptive modelling methods of social networks and show for each of them the resulting useful knowledge on a running example. We particularly emphasize on the most recent methods that combine information available on both the network structure and the node attributes in order to provide original description models taking into account the context

Frequent Pattern Mining in Attributed trees

International audienceFrequent pattern mining is an important data mining task with a broad range of applications. Initially focused on the discovery of frequent itemsets, studies were extended to mine structural forms like sequences, trees or graphs. In this paper, we introduce a new data mining method that consists in mining new kind of patterns in a collection of attributed trees (atrees). Attributed trees are trees in which vertices are associated with itemsets. Mining this type of patterns (called asubtrees), which combines tree mining and itemset mining, requires the exploration of a huge search space. We present several new algorithms for attributed trees mining and show that their implementations can efficiently list frequent patterns in a database of several thousand of attributed trees

Frequent Pattern Mining in Attributed Trees: algorithms and applications

International audienceFrequent pattern mining is an important data mining task with a broad range of applications. Initially focused on the discovery of frequent itemsets, studies were extended to mine structural forms like sequences, trees or graphs. In this paper, we introduce a new domain of patterns, attributed trees (atrees), and a method to extract these patterns in a forest of atrees. Attributed trees are trees in which vertices are associated with itemsets. Mining this type of patterns (called asubtrees), which combines tree mining and itemset mining, requires the exploration of a huge search space. To make our approach scalable, we investigate the mining of condensed representations. For attributed trees, the classical concept of closure involves both itemset closure and structural closure. We present three algorithms for mining all patterns, closed patterns w.r.t. itemsets (content) and/or structure in attributed trees. We show that, for low support values, mining content-closed attributed trees is a good compromise between non-redundancy of solutions and execution time