A Multi-Relational Social Network Mining Tool Based on Logical Programming and Workflows

• Manuel Almeida Fluminense Federal University
• Aline Paes Fluminense Federal University
• Daniel de Oliveira Fluminense Federal University

Abstract

Multi-relational data mining techniques (MDMR) are the most appropriate strategies for dealing with databases containing multiple relationships between non-homogeneous entities, which is precisely the case obtained from social networks. However, the search space for candidate hypotheses of such strategies is more complex than those obtained from traditional data mining techniques. To enable a feasible search in the space of hypotheses, the MDMR techniques adopt language and search biases in the mining process. However, a detailed experimental analysis requires the combination of several distinct parameters, which makes the manual control of such process complex. In this article, we present a tool that instantiates a scientific workflow for the analysis of an MRDM process, modeled from the SciCumulus Workflow Management System, called LPFlow4SN. By controlling the experimental process automatically, LPFlow4SN has the potential of making social networking efficient.

Keywords: Data Mining, Social Network Analysis, Logical Programming, Workflows

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