Advances in FCA-based Applications for Social Networks Analysis

Advances in FCA-based Applications for Social Networks Analysis

Marie-Aude Aufaure (Ecole Centrale Paris, Paris, France) and Bénédicte Le Grand (Université Paris 1 Panthéon-Sorbonne, Paris, France)
DOI: 10.4018/ijcssa.2013010104
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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.
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1. Introduction

Since the development of Birkhoff’s lattice theory in 1940 and the birth of Formal Concept Analysis in the 1980s, concept lattices have been widely used for various purposes in many different applications. 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 reviews recent examples of FCA applications for Social Networks Analysis (SNA). We show that FCA is highly relevant to this field, through various use cases such as buzz monitoring on twitter and identification of leaders (or isolated members) in various online social networks. Our results demonstrate that conceptual metrics derived from FCA are complementary to traditional centrality-based SNA metrics. In addition, FCA is a great tool to identify (overlapping) social communities, as shown in Section 2. Moreover, the expressiveness of Galois lattices may easily be combined to any kinds of semantic representations, such as ontologies; the benefit of these 2 powerful approaches leads to very interesting results, as illustrated in Section 3. The identification of users communities in social networks generally relies on metrics based on the underlying social graphs topology; we present in Section 4 another promising approach, which consists in combining FCA and the traditional topology-based techniques of Social Networks Analysis. Indeed, the solutions coming from those both worlds are complementary –and not contradictory as one might think. Finally, we conclude a FCA-based Visual Analytics tool and present open challenges Formal Concept Analysis to cope with large and dynamic data.

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