Greg Low is trying to find a common nomenclature for edges in graphs:

Positive (Forward) DirectionI’d also like to see the tables use a forward direction naming rather than reverse (like “Written By”). So perhaps:

($from_id) the member

Wrotethe post ($to_id)($from_id) who

Likeswho/what ($to_id)($from_id) the reply to the main post

RepliesTothe main post ($to_id)

Avoid passive voice. That’s good advice in general.

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.

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 ofcentrala 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.

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.

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.

Kevin Feasel

2017-07-10

Graph, Naming