Graph-o-mania: The Flowering of a New Visual Paradigm in Business Analytics
James Kobielus 06000021Q7 email@example.com | 2013-02-14 12:24:11.0 | Tags:  analysis big graph data; | 0 Comments | 5,246 Visits
Graph analysis is all the rage these days, and not just among
data scientists. It's not a new discipline, but, to the larger business and
consumer public over the past few years, it has seemingly come out of nowhere.
Graph analysis is,
at heart, a mathematical
approach for mapping complex relationships among networks of nodes. Graph
modeling is an established branch of statistical modeling that focuses on
mining, mapping, visualizing, and exploring connections, interactions, and
affinities. What distinguishes graph analysis is a focus on "graphs,"
which are abstract networks of relationships (known as "links") among
"nodes" (which may be individuals, groups, companies, products,
systems, objects, concepts, words, and other entities). In addition to
applications in social and semantic applications, graph analysis has
well-established uses in scientific, engineering, and other domains.
Of the technology's many uses, social graph analysis is most
popular, thriving on the gusher of customer intelligence flowing from online
communities of all shapes and sizes. In
addition to customer profiles and other contextual data, modelers may
incorporate a huge range of behavioral information into social graph models.
The behavioral data sources might include Facebook status updates, tweets,
portal clickstreams, geospatial coordinates, transaction records, interest
profiles, call detail records, and usage logs. Social graphs may also
incorporate diverse streams of big data--structured and unstructured, user- and
machine-generated, etc.--that issue from social media as well as from B2C
communities, B2B supply chains, and enterprise applications.
The recent mania for all things "graph" stems in
part from increased use of the word in social-media contexts. Most noteworthy
is Facebook's recent rollout of the
search" feature of its online community. This builds on the "social graph" that
Facebook announced 3 years ago, which maps the explicit and implicit
relationships among members based on their profiles, timelines, and behaviors
within that community.
has stoked the popular buzz for graph analysis, but it's far from the only
appearance of this technology in the mainstream consciousness. The Internet is swarming
with discussions of graphs in every possible big-data, data-science,
digital-marketing, search-optimization, and other application context. Often, graphs
are touted as some sort of secret sauce in new consumer-facing cloud services.
In addition, the business world continues to adopt graph technology, encouraged
by its proven value in anti-fraud, influence profiling,
behavioral segmentation, customer experience management, and other applications.
However, I like to think there's an even simpler reason why graphs have become so popular: they're beautiful to behold. Pictures are worth a thousand words, and graphs, even when they're as densely packed as the Milky Way galaxy, generate some of the most gorgeous analytic visualizations you will ever see. Graph visualizations have an almost magnetic pull on your imagination. If it leverages solid data, a plausible model, and an interactive visualization tool, a graph can immerse you in an open-ended session of navigation, manipulation, and exploration.
Your aesthetics may be different from mine, but I find them mesmerizing. For example, here's a "citation graph" from IBM Research (http://www.research.ibm.com/haifa/dept/imt/papers/jacoviCSCW06.pdf):
Here's the social network graph of one individual, the famed mathematician Paul Erdos, who had no permanent address and was a house guest of one friend after another (http://www.orgnet.com/Erdos.html):
And here's a social graph (http://successfulworkplace.com/2012/11/17/the-social-graph-can-save-your-life/):
You can graph almost any complex data set in ways that are not only lovely to look at but also help the human mind pull out meaningful patterns in a way that pure data, numbers, or other visualizations rarely do. Interactive exploration is where graph analysis truly comes to life, but a static graph can be a thing of deep beauty and meaning in its own right. To see what I mean, type these keywords into Google, click "images" in the bar at the top of the page, and behold the diverse graph visualizations associated with each domain: relationship graph, influence graph, behavioral graph, experience graph, location graph, network graph, affinity graph, and semantic graph.
Even when a graph analysis application eschews pictorial "nodes and connections" visualization, it can still be quite compelling. For example, here's the grid/outline format that Facebook uses to render social graphs.
Graphs draw their power from concise visualization. Even when it balloons to include myriad nodes and connections, a well-crafted graph can focus your mind beautifully on meaningful patterns in the underlying data.