May 7, 2012

Relationship analytics application notes

This post is part of a series on managing and analyzing graph data. Posts to date include:

In my recent post on graph data models, I cited various application categories for relationship analytics. For most applications, it’s hard to get a lot of details. Reasons include:

Even so, it’s fairly safe to say:

Notes on that middle point include:

So I’m tempted to say “it’s all about subgraphs.” But it might be more accurate yet to say “It’s about paths”. Arguably, that’s saying the same thing; paths are subgraphs, and subgraphs are made up of paths, so a way of finding one is also a way of finding the other. But referring to paths nods to such standard tasks as:

Paths are also simpler than subgraphs, and hence also simpler to think about.

Let’s drill down a bit more on the cases of influencer analysis and centrality. Telecom service providers around the world compete with relatively few of their peers (because they’re so geographically bound), and hence are pretty good about sharing technical ideas with each other. One application that has spread like wildfire is influencer analysis for churn control. The idea is to identify influential subscribers who, if they left your service, would be particularly likely to take other people with them, so that you can make great efforts to retain them. The key data used is CDRs (call detail records).

As in many things, it’s tough to separate influencer analysis adoption fact from fiction.

Specific conclusions I’ve heard include:

*For example my Klout profile asserts I’m more influential about Airlines than about Databases or Software. A bit of manual intervention could surely change that — which just serves to underscore my doubts about the effectiveness of social network analytic automation.

One more thing — relationship analytics on social networks rarely works unless you take out a few spurious highly-connected nodes. The paradigmatic example is the local pizza parlor, which receives many phone calls, but is neither a terrorist mastermind nor a major influence upon telecom service churn. More on that point when I write about the partitioning of large graphs.


6 Responses to “Relationship analytics application notes”

  1. Terminology: Relationship analytics | DBMS 2 : DataBase Management System Services on May 7th, 2012 10:07 am

    […] Relationship analytics applications […]

  2. Nick Lim on May 7th, 2012 4:02 pm

    Another area for graph analysis is around the area of “graph partitioning”, or more commonly known as community detection.

    There’s some interest in marketing segmentation that is based on the community of friends that you are a part of, as opposed to segmentation based on common user aspirations and behavior. Treating each community in a coherent marketing communications approach is the end goal.

    Of course, we would suspect some overlaps as birds of the same feather do flock together.

  3. Thomas W Dinsmore on May 8th, 2012 8:23 am

    Looking at the examples, it looks like you’re using the term “graph analysis” to mean the same thing as “link analysis”. That’s fine with me, but if you mean something different you should spell that out.

    For applications like claims fraud, insurers use link analysis/graph analysis to identify anomalous relationships among claimants, appraisers, service providers and policyholders, for example. If an unexpectedly large number of claims include the same parties, it’s a clue that there is a fraud ring operating. A claims fraud monitoring application will kick those claims over to an investigator.

  4. Curt Monash on May 8th, 2012 1:38 pm


    I wouldn’t assume there’s a single, canonical definition of “link analysis”. So I couldn’t give you a precise compare/contrast.

  5. Notes on graph data management | DBMS 2 : DataBase Management System Services on May 13th, 2012 11:36 pm

    […] Relationship analytics applications […]

  6. Introduction to Yarcdata | DBMS 2 : DataBase Management System Services on July 5th, 2012 5:02 am

    […] is still trying to figure out exactly which relationship analytics application areas it is pursuing. Yarcdata’s big multi-year design partner was a large intelligence agency, for […]

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