social network analysis

10月 202016

The study of social networks has gained importance over the years within social and behavioral research on HIV and AIDS. Social network research can show routes of potential viral transfer, and be used to understand the influence of peer norms and practices on the risk behaviors of individuals. This example analyzes the […]

Analyzing social networks using Python and SAS Viya was published on SAS Voices.

11月 212014
Every day there are news stories of fraud perpetrated against federal government programs. Topping the list are Medicaid and Medicare schemes which costs taxpayers an estimated $100 billion a year. Fraud also is rampant in other important federal programs, including unemployment and disability benefits,  health care, food stamps, tax collection, […]
2月 042014
A few Superbowl tweets: "Everything worked out well with the Super Bowl in NY/NJ except the game. What a shocker tonight #TBS_SuperBowl" "Best #superbowl #halftimeshow ever with #brunomars and #redhotchillipeppers . What talent from thats a performer." "Someone turn off the lights to make the #SuperBowl interesting!" In my […]
10月 052012
They say golf is a simple game. The objective is straightforward; get a little white ball into the hole while minimizing the number of shots. Yet, anyone who has ever stepped up to the tee understands that there are many other factors at play. I can’t tell you why, but [...]
8月 312012

 A super hot topic in most organizations is how to make the most of the troves of social data available.

This Post-It Note author isn't specific about the SAS solution that is being used, so I'm going to speculate that he or she is taking advantage of SAS Text Miner, SAS Text Analytics and/or SAS Social Network Analyis. Justin Plumley wrote a fantastic blog post (Voices in the Crowd) about how he mined and categorized the social media discussions that occurred during the 2012 London Olympic Games. In a followup post, Dan Zaratsian shows how to follow social chatter through to the social networks beyond - finding influencers. He uses Social Network Analysis - also an excellent way to track fraudsters

Finally, if you are already a using SAS Text Miner, check out these great updates in the new release - Text Miner 12.1.

Has your company been working to gather information from the social sphere? What methods are you using? Are you thinking of writing a paper for SAS Global Forum about your results!?! GO for it!!

tags: 12.1, Friday's Innovation Inspiration, SAS Text Analytics, sas text miner, social media, social network analysis, twitter
8月 112012
Dan Zaratsian, senior associate technical consultant in the analytics group, is an expert web-crawler—and wakeboarder—who works one-on-one with customers using math to mine sentiment from textual data. An electrical engineer with a mathematical mind, Dan thinks the text analytics he does is awesome—and so do we. Read on for a [...]
7月 172012
With increasing adoption of text analytics, organizations are expecting more from their analysis. They are demanding answers to questions that basic word clouds and niche sentiment analysis tools cannot answer accurately. Not only are they asking how people feel about their products and services and who’s talking about what, but [...]
5月 032012

According to Carlos André Reis Pinheiro, social networks in communications are easy to understand and detect, so Oi Telecommunications chose that route first when trying to detect fraud.

Community detection for fraud proved to be somewhat different. It is a progressive search, from looking at the entire network to looking at a group of customers and then within those groups to find unexpected behaviors - outliers.

Narrow the field

Pinheiro said that the first step is cleansing the data to remove phone numbers that are constants across the network. Those numbers might include the call center number and extensions from international calls.

Secondly, Oi Telecommunications needed to understand how to group people. “If you look at every small connection, every small call, you will have a huge community,” said Pinheiro. “We needed to understand how to divide people into relevant communities; we needed to define some boundaries.”

Tighten the linkage

According to Pinheiro, large networks, such telecommunications networks, follow a power law distribution, meaning they contain a small number of communities with a large number of nodes and the majority of the communities have only a few nodes. You can change the size of the communities by changing the value of resolutions.

Bigger communities mean more members, but weaker strength in the links that connect them. Conversely, smaller communities have fewer members but stronger links among the nodes. This metric, called modularity, is the average number of distinct connections.

“This may take trial and error to get the right modularity at first to solve your business problem – is it fraud, churn?” said Pinheiro. “My average number of distinct connections is 10, so I’ll try to end up with an average number of members in the communities like 10. With high-performance analytics, you can test many different types of communities.”  

Spotting oddballs

In this research, Pinheiro says that Oi Telecommunications collected three months of data. From the data, they could see those customers who seemed to be committing fraud, flag them and follow them through to the next step. “When you talk about social network, we all think about influence. People are influencing others to follow along in some type of event – like churn or a purchase,” said Pinheiro.

The data from the social network analysis show that in the churn field, the viral effect of communities is very real - 11 percent of churners can play as leaders and they can affect 8 percent of the people they are related to.

“When you talk about purchasing, 14 percent of our purchasers can play as leaders and they can influence 17 percent the entire network,” said Pinheiro.

For analysts, the bad news is that there is no viral effect in fraud. Fraudsters create a community with the express purpose of committing fraud. The good news is that there is no viral effect – fraudsters don’t spread their ideas like those who are thinking of changing providers or who’ve found bargain prices. So, according to Pinheiro, he and his team can just change tactics to search for fraud communities.

Pinheiro says that social network analysis now helped them see the differences in the calling patterns within a community. These differences were measured so that outliers became obvious and investigable.

“Fraud is business,” said Pinheiro. “So there is always this behavior to find and investigate.”

Read Pinheiro’s paper, Community Detection to Identify Fraud Events in Telecommunications Networks. Also read Jodi Blomberg's paper,  Twitter and Facebook Analysis: It’s Not Just for Marketing Anymore, about catching criminals using social media. (You can also watch as Anna Brown interviews Blomberg about her paper in this Inside SAS Global Forum video interview.)

tags: churn, fraud, Oi Telecommunications, papers & presentations, SAS Global Forum, social network analysis