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XomniaNewsGraph analytics: What can you achieve with it?

Graph analytics: What can you achieve with it?

More often than not, data scientists work with huge amounts of data that are produced by a business. Typically, they use numerous mathematical, statistical and programmatic techniques to extract valuable information and get untapped value out of data. At some point, 

you might face a challenge where there’s an insufficient amount of historical and/or labeled data. As a result, the majority of knowledge discovery methods can’t be used effectively. Without being able to produce extra information for business inquiries, we tend to move on from the project. However, I’d like to challenge you to look at your data from a different point of view. I can show you the value of graphs by showcasing examples in anomaly detection applications where one is interested in finding the most unusual data occurrences.

A great deal of information can be discovered not only from data records themselves, but also from analysing the relationship between the data points. By trying to understand the relationship between data, analysts can start utilizing more out of what they already have. So-called graphs and graph analysis (a.k.a social networks or networks) can help you to discover different interactions, communications and relationships between one, or multiple data points. This enables you to imply more measures and context onto the data.

What is graph?

Graph (a.k.a social network) is an ordered (for directional graph) or unordered (for undirected graphs) pair of vertices and edges (a.k.a. nodes and links), where vertices can be represented as a circle, and edges as a line connecting circles. The most common graph examples are family trees, tournament brackets, organisational charts, IOT charts, or Facebook and Linkedin friends/colleague networks. Most commonly graphs are interpreted as an adjacency matrix. This means that relationships can be processed by any conventional programming language and stored in any database. Graph data structure represents each data point as an individual instance. in comparison to Entity Relation (ER), the data structure relationship between individual nodes can be unique. Graphs can be seen as a different paradigm to the traditional ER representation. Yet it also comes with its own pros and cons.

Graph methods

Parsing through data records

Parsing through data points are very different as well. GDB are looping through data records in a particular table only once to find the first node in your match statement. Then the match statement is traversed through using list of available outgoing relationships for that particular node. This process is roughly illustrated in the animation below. Traversal through available space using only available paths can significantly increase query performance because engine is only looking at relevant and available neighboring nodes. By limiting maximal hops, you make sure that you’re trying to find only ‘close connections’. Parsing through data points are very different as well. GDB are looping through data records in a particular table only once to find the first node in your match statement. Then the match statement is traversed through using list of available outgoing relationships for that particular node. This process is roughly illustrated in the animation below. By limiting maximal hops, you make sure that you’re trying to find only ‘close connections’. A very amusing example of understanding connection and traversal between two points of interest is called Six Degrees of Kevin Bacon. This game states that any two people are six or fewer links apart from each other.

author avatar
Ashley Howe
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