Abstract
Network data mining has attracted a lot of attention since a large number of real-world problems have to deal with complex network data. In this paper, we present NOESIS, an open-source framework for network-based data mining. NOESIS features a large number of techniques and methods for the analysis of structural network properties, network visualization, community detection, link scoring, and link prediction. The proposed framework has been designed following solid design principles and exploits parallel computing using structured parallel programming. NOESIS also provides a stand-alone graphical user interface allowing the use of advanced software analysis techniques to users without prior programming experience. This framework is available under a BSD open-source software license.
Highlights
Data mining, an interdisciplinary sub eld of computer science, studies the process of extracting valuable information from data by discovering patterns or relationships
We introduce NOESIS (Network-Oriented Exploration, Simulation, and Induction System), a software framework released under a permissive BSD open-source license for analyzing and mining complex networks
Different structural properties for links can be computed by NOESIS, for example, link betweenness, which is the count of shortest paths the link is involved in, or link rays, which is the number of possible paths between two nodes that cross a given link
Summary
An interdisciplinary sub eld of computer science, studies the process of extracting valuable information from data by discovering patterns or relationships. Di erent software tools for analyzing relational data have been developed, according to their main goal and the type of user they are directed to. NOESIS is built on top of a structured parallel programming suite of design patterns, providing a large number of network mining techniques that are able to exploit multiple processing cores available in current microprocessors for a more efficient computation. It is the underlying layer that supports a large collection of network analysis algorithms and data mining techniques, which are described . Different types of networks are dealt with using a unified interface, allowing us to choose the particular implementation that is the most adequate for the spatial and computational requirements of each application Algorithms provided by this subsystem are built on top of the HAL building blocks, allowing the parallelized execution of algorithms whenever possible. An application generator is used to build a complete graphical user interface following a model-driven software development (MDSD) approach. is component provides a user-friendly interface that allows users without programming skills to use most of the NOESIS framework features
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