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Business Network Analytics: From Graphs to Supernetworks

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Book cover Business and Consumer Analytics: New Ideas

Abstract

A large number of problems in business and consumer analytics have input graphs or networks. These mathematical entities have a long standing tradition in discrete applied mathematics and computer science. In many cases, they are the most natural means to represent some type of relationships in data. Consequently, a large number of solution methods based on heuristics and exact algorithms exist for problems that have graphs and/or networks as part of their input. While the number of possible applications of these techniques is not limited to problems in business and customer analytics, we have chosen to present some of them in a survey that would allow newcomers to the field of data science to create some familiarity with the key questions that motivate the area. We have also provided a survey on recent applications and new algorithmic approaches for data analytics. In addition we discuss issues related to the computational complexity of some problems associated with them. Other chapters of this section complement the discussion in this chapter with specific examples of interest or that could motivate new novel research direction and application.

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Notes

  1. 1.

    http://puzzlemuseum.com/month/picm02/200207icosian.htm.

  2. 2.

    http://www.daviddarling.info/encyclopedia/I/Icosian_Game.html.

  3. 3.

    https://archive.org/details/whoshallsurviven00jlmo.

  4. 4.

    https://www.yworks.com/products/yed.

  5. 5.

    www.graphviz.org.

  6. 6.

    https://www.rome2rio.com/.

  7. 7.

    https://www.nada.kth.se/~viggo/problemlist/compendium.html.

  8. 8.

    Klaus von Lampe, Definitions of Organized Crime, Sep. 7, 2017, Available from www.organized-crime.de/organizedcrimedefinitions.htm.

  9. 9.

    https://www.yworks.com/yed.

  10. 10.

    http://stability.matticklab.com.

  11. 11.

    https://www.sciencenews.org/article/eulers-bridges.

  12. 12.

    https://en.wikipedia.org/wiki/Klein_bottle.

  13. 13.

    Commetrix:http://www.commetrix.de.

  14. 14.

    Cuttlefish:http://cuttlefish.sourceforge.net.

  15. 15.

    Cytoscape:http://www.cytoscape.org.

  16. 16.

    EgoNet:https://sourceforge.net/projects/egonet/.

  17. 17.

    Gephi:https://gephi.org.

  18. 18.

    GraphChi:https://github.com/GraphChi/graphchi-cpp.

  19. 19.

    GraphStream:http://graphstream-project.org.

  20. 20.

    Graphviz:https://www.graphviz.org.

  21. 21.

    graph-tool:https://graph-tool.skewed.de.

  22. 22.

    GUESS:http://graphexploration.cond.org.

  23. 23.

    igraph:http://igraph.org.

  24. 24.

    JUNG:https://github.com/jrtom/jung.

  25. 25.

    MapEquation:http://www.mapequation.org.

  26. 26.

    MeerKat:https://www.amii.ca/meerkat/.

  27. 27.

    MuxViz:http://muxviz.net.

  28. 28.

    NetMiner:http://www.netminer.com.

  29. 29.

    NetworKit:https://networkit.iti.kit.edu.

  30. 30.

    NetworkX:http://networkx.github.io.

  31. 31.

    Network Workbench: http://nwb.cns.iu.edu.

  32. 32.

    NodeXL:https://nodexl.codeplex.com/.

  33. 33.

    Pajek:http://mrvar.fdv.uni-lj.si/pajek.

  34. 34.

    Radatools:http://deim.urv.cat/~sergio.gomez/radatools.php.

  35. 35.

    Radalib: http://deim.urv.cat/~sergio.gomez/radalib.php.

  36. 36.

    SNAP:http://snap.stanford.edu/snap.

  37. 37.

    SocNetV:http://socnetv.org.

  38. 38.

    Visone:https://www.visone.info.

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Acknowledgements

P.M. acknowledges previous funding of his research by the Australian Research Council grants Future Fellowship FT120100060 and Discovery Project DP140104183. He also acknowledges previous support by FAPESP, Brazil (1996–2001). P.M. also thanks N.J. de Vries, M.N. Haque, and A.C. Gabardo for discussions and help in preparation of the figures, and Sergio Gómez for useful comments on earlier versions of this chapter and in particular in the area of multilayer networks.

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    Pablo Moscato

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  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Pablo Moscato

  2. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, Australia

    Natalie Jane de Vries

Appendix: Software

Appendix: Software

We list here a set of some free software solutions for the analysis and processing of graphs that we have used in the past and may be worth checking when looking for top-of-the-shelf analysis methods.

  • Commetrix Footnote 13: A software framework for dynamic network visualization and analysis. It supports community extraction, real-time analysis, and visualization of dynamic networks, a better understanding of social network data and clustering of network graphs. An evaluation version of the software (valid for 30 days) is available for download on their webpage. However, the software can also be obtained free of charge after consideration of the user status and affiliation.

  • Cuttlefish Footnote 14: A software framework for visualizing networks. It also supports manipulation of network layouts using different popular algorithms (such as ARF, weighted-ARF, k-core, Kamada-Kawai, Fruchterman-Reingold, iso-m, circle, tree, and radial-tree). This Java-based applet tool can be incorporated into websites.

  • Cytoscape Footnote 15: A software initially designed for Bioinformatics applications, it becomes a popular general platform for graph/network analysis and visualization [285]. It provides an integrated environment for network analysis and visualization. In a nutshell, Cytoscape facilitates graph isomorphism, calculation of MST, network alignment, Social network connectivity analysis, graph triplet counter, topological algorithms, and many other functionalities through apps. An extensive range of apps available to use in Cytoscape, such as for visualization, network generation, graph analysis, clustering, layouts, and network comparisons. Since it was originally written in Java, it can run on all operating systems (Windows, Linux, Mac OS).

  • EgoNet Footnote 16: An open-source and free program for collection and analysis of egocentric social networks. It can help in creating questionnaires, data collection and generating network measures which can be exported to be used by other advanced network analysis tools. Java-based cross-platform executables are available for Linux, Mac OS, and Windows.

  • Gephi Footnote 17: This is a popular suite of tools that allow the exploration and visualization of graphs [18]. It can produce a real-time visualization of networks consisting of up to 100 thousands of nodes and million edges. The layout algorithms provide both the force-and-optimization-based state-of-the-art layout algorithms. In addition to these, it supports the calculation of statistics and metrics from networks. The program runs on all operating systems (Windows, Linux, Mac OS). Many uses, and extensible through plug-ins.

  • GraphChi Footnote 18: A disk-based large graph analysis software which is capable of analysing web-scale of complex networks, including those with billions of edges in a consumer standard laptop computer. It has graph contraction-based implementation for iterative graph algorithms for MST and minimum spanning forest (MSF), PageRank algorithm and also supports streaming graph updates for big graphs. Written in C++ (source code available), it has been tested on Mac OS X and Linux.

  • GraphStream Footnote 19: An open-source Java library for modelling and analysis for dynamic graphs. It provides various options for manipulation and analysis of large graphs. Connected Component, eccentricity, strongly connected components, PageRank are some of the featured graph analysis algorithms available in GraphStream library.

  • Graphviz Footnote 20: An open-source graph visualization software which uses textual description file system, in DOT language. It facilitates flexible options for graph drawing and visualizations. Some examples of features are drawing various types of graphs, visualization using radial, circular, tree and array-based layouts. Apart from visualization algorithms, it also supports graph analysis methods, such as directed acyclic graph, finding biconnected components, connected components, single-source distance filtering, counting graph components finding and augmenting clusters in a graph. The software supports scripting access through API using guile, Java, Perl, PHP, Python, Ruby, and Tcl languages. The source code of the program is available at https://gitlab.com/graphviz/graphviz/.

  • graph-tool Footnote 21: Efficient Python module for manipulation and statistical analysis of graphs [253]. Several graph algorithms such as isomorphism, subgraph, MST, connected components, and maximum flow are supported in graph-tool. It also provides the calculation of standard statistical graph measures and community structure detection algorithms.

  • GUESS Footnote 22: An open-source exploratory data analysis and visualization tool for graphs and networks [3]. It supports many popular layout algorithms such as Fruchtermen-Reingold, Kamada-Kawai, Sugiyama, Random, Radial, and Rescaling and rotating. Written in Java, it can run in all operating systems (Windows, Linux, Mac OS).

  • igraph Footnote 23: Collection of network analysis tools with the importance on efficiency, portability, and ease of use [57]. It is one of the biggest graph libraries written in C, which allows programming in R, Python, and C/C++. It has an extensive range of features, such as graph generation, conversion, manipulation, layout algorithms, calculation of structural statistic and measures, community detection, calculation of cohesive block and coreness, Graph Clustering, and motifs identification.

  • JUNG Footnote 24: Java Universal Network/Graph Framework (JUNG) is an extensible software library for modelling, analysing, and visualizing of graph data. It can generate random graphs, clustering algorithms, calculation of structural measures (such as centrality, PageRank, and HITS), and decomposition of graphs. Its visualization framework consists of Fruchterman-Reingold, Kamada-Kawai, Radial, Tree, Circle and Spring Layout.

  • MapEquation Footnote 25: Tools to simplify and highlight important structures in complex networks [79]. Centred on community detection using the Infomap algorithm. It has single and multi-level hierarchical layout algorithms for visualization. Written in C++, it can run on all operating systems (Windows, Linux, Mac OS).

  • MeerKat Footnote 26: A tool to visualize and analysis of social network. It includes several network analysis capabilities such as community detection, layout, and dynamic analysis of networks. Meerkat facilitates several state-of-the-art layout algorithms: such as Fruchterman-Reingold, Circle, Self-Organizing, and Kamada-Kawai. It also provides traditional graph metrics, or statistical information and well-known network measures like PageRank, HITS, Betweenness, and Centrality. The installer is available for Windows, Ubuntu, and Mac OS X.

  • MuxViz Footnote 27: Framework for the multilayer analysis and visualization of networks [61]. Although it is designed for multilayer networks, single layer networks can also be used as a valid input. It supports Fruchterman-Reingold, Kamada-Kawai visualization for single layer graph and supports topological descriptors calculation for both types of networks. Based on R and Octave, it can run on all operating systems (Windows, Linux, Mac OS).

  • NetMiner Footnote 28: A proprietary software for network visualization and analysis. It has an inbuilt Python-based automatic script generation that facilitates the work for some users. It supports built-in 3D network visualization and graph mining based on Machine Learning for reduction, classification, clustering, and recommendation. An academic time-limited licence is available to students, researchers, and teachers. The software only works on Windows operating system.

  • NetworKit Footnote 29: An open-source toolkit for large-scale network analysis. It contains the scalable implementation of graph algorithms to utilize multicore architecture. This Python-based toolkit used OpenMP with C++ for shared-memory parallelism. It has several node centrality measures such as degree, k-core decomposition, PageRank, and Betweenness. The partition algorithms are employed for community detection, connected component identification, and k-core decomposition. It also has several graph modelling and generation approaches. The software requires Linux based environment (such as Mac OS X and Ubuntu). However, the native Linux Subsystem supported by Microsoft is sufficient for using it in Windows 10.

  • NetworkX Footnote 30: a Python software package which facilitates the creation, manipulation, and study of the structure, dynamics, and functions of complex networks [282]. It provides tools for social, biological, and other network analysis. It has a wide range of algorithms for approximation and heuristics (k-component, clique, clustering, dominating set, independent set, Steiner tree, vertex cover, etc.). NetworkX facilitates the computation of bi-partiteness (matching, projection, and centrality are few of them) and components (strong, weak, biconnected, semi-connectedness, etc.). Among other features we can mention connectivity with k-edge, k-node, disjoint path, min cut, etc.; cores decomposition using k-core, k-crust, k-corona, etc.; network flows and link analysis (PageRank, Hits) are some highlights.

  • Network Workbench Footnote 31: A toolkit for large-scale network visualization, analysis, and modelling specially designed for biomedical, social science, and physics research. In terms of network analysis, it supports Random, Watts-Strogatz Small world, random and scale-free methods for graph modelling; tree, balloon, radial, circular, Kamada-Kawai, Fruchterman-Reingold, Force-Directed, etc. layout algorithms; community detection, clustering, PageRank and other standard network analysis approaches. The installer is available for Windows, Linux, and Mac OS X.

  • NodeXL Footnote 32: An open-source template for Microsoft Excel capable of network visualization and analysis. Apart from general network visualization, it is capable of supporting text, sentiment, and social media stream data analysis. It is also capable of calculating network metrics like degree, centrality, PageRank, and clustering coefficients. Dynamic filtering, vertex grouping, Zooming, and scaling are some mentionable features available in the graph layout approaches.

  • Pajek Footnote 33: Analysis and visualization tool for Windows (can be run under Linux and Mac OS X using Wine) [19]. Includes the generation of random networks, the calculation of structural network descriptors, the operation of networks, community detection, etc. It also includes features for subnetwork extraction, searching for connected components, k-neighbours, max flow, community detection, generation different types of random networks. Several popular layout algorithms like Fruchterman-Reingold, Kamada-Kawai, FishEye transformation, etc. are available.

  • Radatools Footnote 34: Set of programs for the analysis of complex networks, with main attention to community detection and the finding of structural properties [108]. Written in Ada (source code available as RadalibFootnote 35), it includes native executables for Windows, Linux, and MacOS. It includes the modularity and mesoscales approaches for community detection; weighted network conversion; computation of connected components, degrees, strengths, clustering coefficients, and so on. To support network manipulation functionalities, it provides subgraph extraction, network data type conversion, sorting of nodes based on degree, spanning tree computation, etc.

  • SNAP Footnote 36: General purpose, high performance system for analysis and manipulation of large networks [171]. Written in C++, yet can be accessed from C++ and Python. It can scale the algorithms to handle network consisting of hundreds of millions of nodes and billions of edge. It has a wide range of features for supporting different types of graphs, graph modelling features, graph manipulation, drawing, community detection, and analysis. Some of the notable features of them are subgraph conversion, connected component, clustering coefficient calculation, BFS and DFS and k-core decomposition.

  • SocNetV Footnote 37: Social Network Visualizer (SocNetV) is a cross-platform, free software for network analysis and visualization. It has rich feature set ranging from network visualization, layout modelling, network measures calculation, fast algorithms for community detection (clique and triad census) and built-in web crawler for creating the social network. It has embedded many popular social network datasets into the package, such as Zachary, Freeman, Mexican Power Network, and Petersen, and also supports several random network generation approaches. It features the calculation of cohesion metrices (Geodesics Matrix, Eccentricity, Symmetry Test, etc.), connectedness (strongly, weakly connected, and disconnected) and visualization layouts (circular, Spring Embedder, Fruchterman-Reingold, etc.). The source code (http://socnetv.org/downloads) and the installer are available for Windows, Linux, and Mac OS X.

  • Visone Footnote 38: Tool for the analysis and visualization of social networks [35]. It has features for calculation of node and link-level indices from the graph, such as centrality (Degree, Betweenness, Closeness, Current-flow centralities, Eccentricity, Eigenvector centrality, PageRank, etc.) and clustering coefficient for local density calculation. Written in Java, it can run on all operating systems (Windows, Linux, Mac OS).

Many other options for specific purposes exist, e.g., community detection algorithms, analysis of specific dynamics, benchmarking, etc. The previous list tries to cover only general purpose programs, but a simple online search is enough to discover other interesting tools. There are also complex network packages for proprietary platforms, such as Matlab, that we have not considered in the previous list. The integration of some tools with Python (igraph, NetworkX, graph-tool) and R (igraph, MuxViz) is very useful to expand their possibilities to analyse complex networks using high level programming.

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Moscato, P. (2019). Business Network Analytics: From Graphs to Supernetworks. In: Moscato, P., de Vries, N. (eds) Business and Consumer Analytics: New Ideas. Springer, Cham. https://doi.org/10.1007/978-3-030-06222-4_7

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