NOESIS

NOESIS is an open source framework for network data mining that provides a large collection of network analysis techniques, including the analysis of network structural properties, community detection methods, link scoring, and link prediction, as well as network visualization algorithms. It also features a complete stand-alone graphical user interface that facilitates the use of all these techniques. The NOESIS framework has been designed using solid object-oriented design principles and structured parallel programming. As a lightweight library with minimal external dependencies and a permissive software license, NOESIS can be incorporated into other software projects. Released under a BSD license, it is available from http://noesis.ikor.org/download/.

What is NOESIS?

[Wikipedia] (Greek: νόησις for "insight") "understanding as the ability to sense, or know something, immediately".

... noesis is identified as noetic with the nous being immediate or intuitive thinking and it is contrasted to dianoia (διάνοια) which is rational or discursive thinking

Noesis — not an abstract concept or a visual image, but the act or function of the intellect (q.v.) whereby it apprehends spiritual realities in a direct manner.

[Wiktionary] "Husserl calls the noesis the meaning-giving element of the act, and the noema he calls the meaning given in the act."

Etymology: From Ancient Greek νόησις (noēsis), “‘concept”, “idea”, “intelligence”, “understanding’”), from νοεῖν (noein), “‘to intend”, “to perceive”, “to see”, “to understand’”) (from νοῦς (nous), “‘mind”, “thought’”), from νόος (noos)) + -σις (-sis), suffix forming nouns of action.

NOESIS description

Víctor Martínez, Fernando Berzal Galiano & Juan Carlos Cubero Talavera: NOESIS: A Framework for Complex Network Data Analysis. Complexity, Volume 2019, Article ID 1439415, https://doi.org/10.1155/2019/1439415, October 2019.

NOESIS main features

The NOESIS open-source framework for network data mining provides a large collection of network analysis techniques, including the analysis of network structural properties, community detection methods, link scoring and prediction techniques, and network visualization algorithms. NOESIS includes a complete graphical user interface, but it can also be included in other software projects as a lightweight library, since it is distributed under a permissive BSD license.

• Multiple network analysis measures: degree, eccentricity, average path length, closeness, decay, betweenness, PageRank, HITS, eigenvector centrality, Katz centrality, clustering coefficient, connected components, link betweenness, link embeddedness, link neighborhood overlap...
• Different supported network file formats: GML, GraphML, and GDF, among others. Networks can be exported to some image formats, including SVG, PNG, and JPEG format.
• A large number of random network models: Erdös-Renyi, Gilbert, Watts-Strogatz, Barabasi-Albert, and Price models. Other models, including regular models, are also available.
• Different community detection methods: Kernighan-Lin, Newman-Girvan, Radicchi, hierarchical, fast greedy, spectral, or BigCLAM community detection, among others.
• Several link scoring and prediction scores, such as Adamic-Adar score, Resource Allocation index, Katz score, or different random walk-based metrics.

The NOESIS Network Analyzer

Even though NOESIS is provided as a lightweight library to be reused in other software development projects, it also includes an easy-to-use graphical user interface. The NOESIS Network Analyzer can be used as an alternative to well-known SNA tools such as Gephi, Pajek, UCINET, or NodeXL. You can load and visualize your own networks and use all the data mining techniques implemented in the NOESIS framework using its GUI. Network visualization can be customized for nodes and links based on their attributes and different network layout algorithms are also available.

External dependencies

The NOESIS framework relies on the iKor library of reusable software components, which provides a customizable collection framework, support for the execution of parallel algorithms, mathematical routines, and the application generator used to build the NOESIS GUI. This is the only external dependency of the NOESIS framework, a deliberate decision to keep it lightweight and facilitate its integration in other software projects.

Software license

NOESIS is distributed under the Simplified BSD License below:

Copyright (c) 2015-2019, Fernando Berzal (berzal@acm.org) & Víctor Martínez (victormg@acm.org). All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

BSD licenses are a family of permissive free software licenses, imposing minimal restrictions on the redistribution of software. This license allows unlimited redistribution for any purpose as long as its copyright notices and the license's disclaimers of warranty are maintained. BSD Licenses allow proprietary use and allow the software released under the license to be incorporated into proprietary products (i.e. works based on this material may be released under a proprietary license as closed source software). See Wikipedia.