Multi-level Network Construction Based on Intelligent Big Data Analysis
In this work, we present a hybrid miner-network analyzer (HMNA) system includes three main stages; the first stage called preparing and preprocessing stage that includes building initial network from citation file and Find Keywords through apply Rake and
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Department of Computer Science, Faculty of Science for Women (WSCI), University of Babylon, Babylon, Iraq {samaher,mahdi.salman}@uobabylon.edu.iq Abstract. In this work, we present a hybrid miner-network analyzer (HMNA) system includes three main stages; the first stage called preparing and preprocessing stage that includes building initial network from citation file and Find Keywords through apply Rake and cleaning. The second stage involves building classification model including parameters detection and apply LDA for find topics, final stage, add the topics of document into initial network to construction multi-layer network, where each level represent community related of that topic. We can summarize the main points of HMNA system as: (i) It deals with real, complex, huge database of papers ‘citation’. (ii) The preprocessing stage involves retrieve keywords from corpus using Rake after add constructions on it and cleaning without using any feature selection method. (iii) It building digital corpus that combines with dictionary to clustering the clean text into multi groups based on LDA model. (iv) It reconstructed the initial network by add the labeling of topic results from above step to it. (v) It builds multi communities (multi-level network), each level in that network represent single communities, (vi) It computes the main characteristics measures for each level or communities to determine the similarity in it structure with cluster in citation network. Keywords: Intelligent data analysis Big data Text mining Complex network Citation network Characteristics network measures RAKE LDA
1 Introduction Recently, the logical community has realized that there are a few systems, both normal and artificial, which cannot be completely caught on by a reductionist approach (i.e., by analyzing their constituting components in a confined way). On the contrary, their simply visible properties appear to be characterized by the structures of intuitive between these components such frameworks are presently called complex Network. Cases have been found in many example logical fields (e.g. in social or transport systems, Web. Most surprising one of the cases a complex network is the brain; it is composed of more than 100 billion neurons, each one of them appears a very simple dynamic, the human capacity for thinking only emerges when these simple dynamics start to interact) [1]. © Springer Nature Switzerland AG 2019 Y. Farhaoui and L. Moussaid (Eds.): ICBDSDE 2018, SBD 53, pp. 102–118, 2019. https://doi.org/10.1007/978-3-030-12048-1_13
Multi-level Network Construction
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Now, there are two approaches utilized to extricate data from complex networks as explained in Fig. 1; classical data mining methods, and complex network analysis. Born within physics, with considerable inputs of science and insights, the hypothesis of complex networks has demonstrated to be a capable device for the analysis of complex frameworks; it permits reducing them into basic structures of intelligence, which can effectively be examined
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