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Category: Graph

R’s iGraph + SQL Server Graphs

Published 2017-07-07 by Kevin Feasel

Dennes Torres has a post which shows how to use R’s iGraph library to visualize graphs created in SQL Server 2017:

The possibility to use both technologies together is very interesting. Using graph objects we can store relationships between elements, for example, relationships between forum members. Using R scripts we can build a cluster graph from the stored graph information, illustrating the relationships in the graph.

The script below creates a database for our example with a subset of the objects used in my article and a few more relationship records between the forum members.

Click through for the script.

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Tidygraph

Published 2017-07-07 by Kevin Feasel

Thomas Lin Pedersen announces tidygraph, a tidyverse library for dealing with graphs and trees in R:

One of the simplest concepts when computing graph based values is that of
centrality, i.e. how central is a node or edge in the graph. As this
definition is inherently vague, a lot of different centrality scores exists that
all treat the concept of central a bit different. One of the famous ones is
the pagerank algorithm that was powering Google Search in the beginning.
tidygraph currently has 11 different centrality measures and all of these are
prefixed with centrality_* for easy discoverability. All of them returns a
numeric vector matching the nodes (or edges in the case of
centrality_edge_betweenness()).

This is a big project and is definitely interesting if you’re looking at analyzing graph data.

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Building Graph Tables

Published 2017-06-29 by Kevin Feasel

Tomaz Kastrun uses a set of e-mails as his SQL Server 2017 graph table data source:

To put the graph database to the test, I took bunch of emails from a particular MVP SQL Server distribution list (content will not be shown and all the names will be anonymized). On my gmail account, I have downloaded some 90MiB of emails in mbox file format. With some python scripting,  only FROM and SUBJECTS were extracted:

writer.writerow(['from','subject'])
for index, message in enumerate(mailbox.mbox(infile)):
    content = get_content(message)
    row = [
        message['from'].strip('>').split('<')[-1],
        decode_header(message['subject'])[0][0],"|"
          ]
    writer.writerow(row)

This post walks you through loading data, mostly.  But at the end, you can see how easy it is to find who replied to whose e-mails.

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Graph Database In SQL Server

Published 2017-06-05 by Kevin Feasel

Niko Neugebauer has started a new series, this time on graph database functionality in SQL Server 2017:

Graph databases are based on graph theory, and employ nodes, edges, and properties. The graph theory is the study of the graphs that are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of nodes, edges which are connected by edges, arcs, or lines.
A graph can be directed or undirected (uni or bi-directional) that might point the direction of the relationship between the edges.
Graph databases can be compared to the Network Model Databases, that were focusing on solving the same problem the interconnected world.

The most popular graph database in the world currently is NEO4J, which is implemented in Java and is using CQL (Cypher Query Language), a language that has definitely inspired the SQL Graph T-SQL language extension.

Niko notes that this is not a fully mature product yet, but it’s an interesting start.

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