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Mining Uncertain Graphs: An Overview

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Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10230))

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Abstract

Graphs play an important role in modern world, due to their widespread use for modeling, representing and organizing linked data. Taking into consideration that most of the “killer” applications require a graph-based representation (e.g., the Web, social network management, protein-protein interaction networks), efficient query processing and analysis techniques are required, not only because these graphs are massive but also because the operations that must be supported are complex, requiring significant computational resources. In many cases, each graph edge e is annotated by a probability value p(e), expressing its existential uncertainty. This means that with probability p(e) the edge will be present in the graph and with probability \(1-p(e)\) the edge will be absent. This gives rise to the concept of probabilistic graphs (also known as uncertain graphs). Formally, a probabilistic graph \(\mathcal{G}\) is a triplet (V, E, p) where V is the set of nodes, E is the set of edges and \(p: E \rightarrow (0,1]\). The main challenge posed by this formulation is that problems that are relatively easy to solve in exact graphs become very difficult (or even intractable) in probabilistic graphs. In this paper, we perform an overview of the algorithmic techniques proposed in the literature for uncertain graph analysis. In particular, we center our focus on the following graph mining tasks: clustering, maximal cliques, k-nearest neighbors and core decomposition. We conclude the paper with a short discussion related to distributed mining of uncertain graphs which is expected to achieve significant performance improvements.

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Notes

  1. 1.

    Although existential probabilities can be assigned to the vertices of the graph as well, in this paper we focus on edge probabilities only.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Vasileios Kassiano, Anastasios Gounaris, Apostolos N. Papadopoulos & Kostas Tsichlas

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  1. Vasileios Kassiano
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  2. Anastasios Gounaris
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  3. Apostolos N. Papadopoulos
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  4. Kostas Tsichlas
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Correspondence to Apostolos N. Papadopoulos .

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  1. Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, Victoria, Australia

    Timos Sellis

  2. Informatics, Ionian University, Kerkyra, Greece

    Konstantinos Oikonomou

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Kassiano, V., Gounaris, A., Papadopoulos, A.N., Tsichlas, K. (2017). Mining Uncertain Graphs: An Overview. In: Sellis, T., Oikonomou, K. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2016. Lecture Notes in Computer Science(), vol 10230. Springer, Cham. https://doi.org/10.1007/978-3-319-57045-7_6

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