Advances in FCA-based Applications for Social Networks Analysis

Abstract

Concept lattices have been widely used for various purposes in many different applications since the 1980s. Recent applications of Formal Concept Analysis include extensions of traditional FCA applications such as data and text mining, machine learning and knowledge management. Progress has also recently been made in software engineering, Semantic Web and databases. New applications have also emerged in the fields of healthcare, ecology, biology, agronomy, business and social networks. This article presents example of successful applications of FCA for Social Networks Analysis. We show the benefit of FCA solutions, as well as their combination with semantics and topology-based approaches. We conclude by presenting FCA-based visualization solutions and open challenges for FCA in the context of large and dynamic data.

Knowledge discovery in social networks by using a logic-based treatment of implications

“…As stated in the introduction and at the beginning of this section, there are works focused on detection of communities within social networks [38], others focus on the analysis of relations among individuals, for instance, detecting opinion leaders [10,27,41], or on the detection of tokens and the relationship among individuals based on key words [13,23], or on the visualization as a network of a set of tags found in a social network [5]. In any case, and as far as we know, there is not a similar proposal in the line presented in this work.…”

“…Thus, in [28] an ontology-based technique is proposed for a more fine-grained sentiment analysis of Twitter posts. Concerning social networks, FCA has been considered a great tool to study social communities [12]; in [5] the authors consider objects as members and their attributes as their contacts and build the formal context on which the concept lattice is generated. This lattice is then used to calculate statistics, which the authors call Conceptual Relatedness and Closeness, about every member of the social network.…”

Canonical Computational Models Based on Formal Concept Analysis for Social Network Analysis and Representation

“…It is also possible to apply the minimal canonical basis in large databases, since the canonical base is complete, and its smaller storage costs are very sought after. Aufaure and Grand [4] shows several approaches that use FCA in the analysis of social networks and discuss a recurring difficulty for the subject analysis. Then, as future work we pretend to explore techniques to reduce the size of the concept lattice.…”

“…A survey of current FCA applications was presented in [3], in which it can be observed that social networks have not been a very deeply explored subject in the domain. In [4] several approaches that use FCA in the analysis of social networks are presented. However, the paper also shows a recurring difficulty for the subject, which is dealing with amounts of data so large that processing and visualization become challenging.…”

Using implications from FCA to represent a two mode network data

“…Aufaure and Le Grand [17], who describe lattice expressiveness, especially when associated with ontologies, as a benefit of this FCA use. The work is a compilation of case studies.…”