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From DSC, Visualize your Social Media Analytics by Manoj Sharma

" ... Network Graph Analysis can be used to identify the most important participants based on their degree of interaction with other social media users for the topic of interest. Centrality in a graph can be used to identify the most vertices in a graph which translates into the most influential person in a social network. ....
For example, in the Twitter network graph (built using Python NetworkX and d3.js) shown here for @Walmart, we see a strong tweet connection with Huggies. Network graphs like the one above can be used to study interactions in social media which may lead to insights that can be useful in the real world. ... " (click below for image enlargement)

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From DSC, Visualize your Social Media Analytics by Manoj Sharma " ... Network Graph Analysis can be used to identify the most important participants based on their degree of interaction with other social media users for the topic of interest.

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Google Images. The most comprehensive image search on the web.

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1. Lines should be straight. (Yes)
2. Lines should be far apart from one another. (For the most part yes they are. In certain areas they are not)
3. Lines should not cross or touch. (Yes)
4. Lines should be easy to follow from one node to another. (Yes color helps with this)
5. Nodes that connect should be close. (Yes)
6. Nodes that are most central should be close to the center of the graph. (Yes the central node is center)
7. Nodes that are similar in some way should be placed nearby one another. (Yes)

I really liked how this network graph showed plenty of color. This made it easier for me to understand the difference. This graph represents a twitter network graph and @walmart tweets. As you can see it has a strong connection with huggies. This graph is used to study interactions over social media. I found this one to be very interesting because I am a twitter person myself.
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From DSC, Visualize your Social Media Analytics by Manoj Sharma

" ... Network Graph Analysis can be used to identify the most important participants based on their degree of interaction with other social media users for the topic of interest. Centrality in a graph can be used to identify the most vertices in a graph which translates into the most influential person in a social network. ....
For example, in the Twitter network graph (built using Python NetworkX and d3.js) shown here for @Walmart, we see a strong tweet connection with Huggies. Network graphs like the one above can be used to study interactions in social media which may lead to insights that can be useful in the real world. ... " (click below for image enlargement)

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From DSC, Visualize your Social Media Analytics by Manoj Sharma " ... Network Graph Analysis can be used to identify the most important participants based on their degree of interaction with other social media users for the topic of interest.

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This week, I have a network graph of different brands. There's no color, so there isn't much visualizing. It isn't great to look at, and the ties can become confusing because there isn't much to differentiate from.
For the most part, this network follows Kozo's rules.
1. Lines are straight (yes)
2. Lines are far apart from one another (sort of. they are kind of close, but the network stretches)
3. Lines don't cross or touch (yes)
4. Lines are easy to follow from one node to another (for the most part-color might help this)
5. Nodes that connect are close (yes)
6. Nodes that are most central are close to the center (the central node is leaning left)
7. Nodes that are similar are in a way placed nearby each other (yes)

I don't like the lack of color-it makes following the lines kind of difficult. I do like how they connect each brand to one another and the data it implies-like how Apple and Samsung are connected.
-Betelgeuse

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Shown below is a graph for network visualization.  It reflects a study on reshipping scams and from what I can see, follows Kozo's rules about graph:
1. Lines are straight.
2. Lines are far apart (for the most part) from one another.
3. Lines do not cross or touch.
4. Lines are easy to follow from one node to another.
5. Nodes that connect are close.
6. Nodes that are most central are close to the center
7.  Nodes that are similar are in a way placed nearby each other such as the cities in relation to the USA node.

I like this example because it shows how what we learned in class can be applied in the work force.

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GraphConnect 2014 Sponsor, Linkurious, discuses mapping fraud with Neo4j

Originally posted on the Linkurious blogDrug lords are not the only persons using mules to launder money. eCommerce operations are confronted to "reshipping scams". In these scams, mules are used by online fraudsters to turn their credit cards into actual goods. Graphs can help detect this fraud.

When a job ad turns you into a money laundering mule

We have already discussed about credit card fraud on this blog by addressing how credit cards get stolen. But do you know what happens next? If you have been frequenting the internet at any point during the past 10 years you may have come into contact with a job ad for a re-shipping position. Reshipping is used by fraudsters to launder the money from their stolen credit card. It works like this :

the criminals steal credit cards information ;

they buy goods on ecommerce websites ;

the goods are sent to a third party (the mule) ;

the third party receives the goods and re-ships them to the criminal (hence the term reshipping);

the criminal sells the goods and receives cash ;

The third party, recruited via a job ad promising a generous compensation, acts as mule. His role is twofold. First, the mule helps deceive the ecommerce website. If the fraudster was asking for a delivery in Ukraine or Nigeria, it would probably ring some bells. However if during his checkout he asks for his goods to be delivered to an address in the US, it will seem normal. The second role of the mule is to isolate the fraudster from investigations. When the ecommerce website, the credit card provider or the credit card holder detect the fraudulent transaction, they will turn to the mule. The mule will have to prove that he/she did not steal the credit card in the first place. Meanwhile the fraudster will be safe and sound, enjoying his cash in another country. The most sophisticated reshipping organizations work as white-label money laundering services. The US authorities are noticing this : they have severely cracked down on reshipping rings recently. The rationale behind this is simple : make it harder to launder money and the incentive to commit fraud will decrease. Soldiers like to talk about the " kill chain ", the series of stages involved in an attack. A defense only have to stop the attack at one stage of the kill-chain to be successful.
Money-laundering is the last stage in credit card fraud...and the last opportunity to act before it is too late. We are going to see how ecommerce websites can identify reshipping scams and save money.

Looking for suspicious patterns in online transactions

Let's start by considering the information a ecommerce website may have. For any given order it stores (among other things) :

an order ID ;

various order information : date, content, total ;

a billing address (composed of a street, a city and a country) ;

a shipping address (composed of a street, a city and a country) ;

an IP address (that can be geolocalized and is thus linked to a city and country) ;

The information can be modeled as this :
There is a couple of things we can do with that data to identify fraud. A first step might be to compare the billing and shipping addresses. A difference between a billing and a shipping address might be indicative of a reshipping scam. Furthermore we can look into the IP address. If the IP address localization does not match the billing address or the shipping address, the situation is highly suspicious. We are going to see how to perform these security checks with a graph database.

Concrete example

I have prepared a small dataset with a few (fake) ecommerce orders. It includes regular transactions and fraudulent transactions. The data has been loaded into a Neo4j graph database. First of all, we are going to see the list of the transactions in our dataset. To do that we can type into the Neo4j console (or within Linkurious) :

MATCH (orders:Transaction)

RETURN DISTINCT orders.date as date, orders.items as items, orders.amount as amount

We have 5 results.

Now we want to return all the transaction that have a different billing and shipping address addresses :

MATCH (address1:Address)-[IS_SHIPPING_ADDRESS]->(suspiciousorder:Transaction)<-[:IS_BILLING_ADDRESS]-(address2:Address)

RETURN DISTINCT suspiciousorder

We have 3 results : the $99, $499 and $878 transactions. These transactions are suspicious and could be flagged as such for further investigation. Even more suspicious are the transactions where the IP address is coming from a location different of the other addresses. We can identify that pattern by typing the following query :

MATCH (a:Transaction)-[r*2..3]-(b:City)

WITH a, COUNT(DISTINCT b) AS group_size, COLLECT(DISTINCT b) AS cities

The alert brings our attention to an IP located in Lagos, Nigeria. This is a known hotbed for online fraud. The IP address is associated to two transactions. In both of these transactions, the billing address and the shipping address are different : this is indicative of a reshipping scam. We can see this in the graph visualization below. The countries are in dark green, the cities in blue, the IP address in red, the street in orange, the orders (with their date) in light green and the addresses in asparagus.

Network visualization helps us understand the link between the 2 orders, the IP address in Nigeria, the address in Detroit and the credit card owners in San Francisco. Network visualization allows us to turn the alert triggered by the detection pattern we wrote into something more. Now we know that our ecommerce operation is used by a scam artist in Nigeria who is using (unwilling?) accomplices in the US to launder his money. We can freeze any further transactions from the IP address and alert the authorities to investigate the US accomplices. Of course the data we have used here is fake. Furthermore, the fraudsters could use more advanced techniques (starting with a simple proxy for example) to avoid detection. Nevertheless, the same approach (tweaked) of identifying fraudulent patterns and looking for them can be used successfully to fight against reshipping and ecommerce fraud. Reshipping scams are used to launder money for various criminal activities. We have seen that ecommerce websites can use their data to identify potentially fraudulent transactions before it is too late.

GraphConnect 2014 SF

Linkurious will be a sponsor and panelist at GraphConnect 2014 SF. GraphConnect is the world's only conference focused on the rapidly growing world of graph databases and applications. Join over 700 graphistas from around the world on October 22, 2014 at SFJAZZ in San Francisco to hear more from Linkurious, as well as from companies like UBS, eBay, ConocoPhillips, CrunchBase and more. Visit www.graphconnect.com to register and get more information.

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Reshipping Scams and Network Visualization - Neo4j Graph Database

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Originally posted on the Linkurious blog Drug lords are not the only persons using mules to launder money. eCommerce operations are confronted to "reshipping scams". In these scams, mules are used by online fraudsters to turn their credit cards into actual goods. Graphs can help detect this fraud.

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I think the following network graph is supposed to represent the relationships between the characters in the "Wizard of Oz". I found that it violates several of Sugiyama's guidelines. First of all the lines are not straight. The lines are not far apart and they certainly touch and cross each other. I also think it is difficult to see where all the lines go even though they are in different colors. Looks confusing to me...would be even more so if I hadn't seen the movie!

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I've found this network graph which I really like because it is so simple! It is easy to see the connections between the nodes. I have looked at other network graphs from students and some of them seem so complex! I think this graph is perfect for someone who is just learning about network graphs. I do believe that it follows Kozo Sugiyama's guidelines. I think it is a good network and is easy to follow.

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I realize that one of my classmates already posted a network graph similar to the one I chose, but this one is a little different. For starters, the network graph below is a graph without direction. Although the two graphs include nodes with the same names, having no direction is the main difference.
I do believe the network graph I chose to display follows Kozo Sugiyama's guidelines-- visually and technically. I wanted to find a simple graph to share and I feel as though this one is very easy to understand. Having a list of the nodes and edges present also makes it super easy to follow the graph!

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I think this graph is a great one, and on his page he has some others that he has done that are sub-categories of this, like males only. It truly groups his range of friends, the red being high school friends (with obviously many ties between those ones because they all went to school together, the blue being his college friends and the obvious increased ties between them for the same reason as the red, the yellow are his girlfriends friends that he has made friends with all the way-can see how he met his girlfriend in college and her friends are separate than those or his high school friends as they are probably from another area separate than high school and college. And the smaller groups are people he has met from trips and networking along the way. The clusters are clearly defined, the colors relational, and it's overall visual appearance is organized and flows. Almost none of the Kozo guidelines are 100% applicable but close...like the lines are a "line" that is straight but the slight curve is needed to show multiple ties without crossing other as much as possible. Very Cool. https://griffsgraphs.wordpress.com/tag/social-network/

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This is a simple network graph. It has clearly labeled nodes, and lines are straight. I think that this picture follows the guidelines fairly closely. With the exception of crossed lines, I do not think this makes it confusing though I can still clearly make out all the intended connections. I think it is very clear, and simple (it does help to have the edge list and node labels off to the side) but even without referencing these I think the point gets across.

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This image shows which mega-companies own other smaller companies. I have seen this image time and again, and I have always considered it to be the type of image that is sort of confusing. This image shows that 7 major companies own dozens of other smaller companies. Whenever I've seen images like this, I always think of the term "monopoly" because it really does show how many individual nodes are owned (or directed) by so few other nodes. Understanding social networks help me to understand these ties and connections between companies.
I think this model generally follows Sugiyama's guidelines, being that the ties are quite clear, but the problem with those guidelines is that it becomes much, much more difficult to comply when you begin adding more and more nodes and ties. You simply cannot expect a network with this many connections to be what Sugiyama demands.
I think this is a good representation of a social network graph because it shows which companies are owned, which companies own, and which companies that own and are owned.

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It may be obvious that Corn Flakes and Frosted Flakes are both made by Kellogg's, but did you know that Hot Pockets and L'Oreal share a parent company in Nestlé?

A ginormous number of brands are controlled by just 10 multinationals, according to this amazing infographic from French blog Convergence Alimentaire. Now we can see just how many products are owned by Kraft, Coca-Cola, General Mills, Kellogg's, Mars, Unilever, Johnson & Johnson, P&G and Nestlé.

(Disclaimer: We are not sure how up-to-date the graphic is. For example, it has not been updated to reflect P&G's sale of Pringles to Kellogg's in February.)

It's not just the consumer goods industry that's become so consolidated. Ninety percent of the media is now controlled by just six companies, down from 50 in 1983, according to a Frugal Dad infographic from last year. Likewise, 37 banks merged to become JPMorgan Chase, Bank of America, Wells Fargo and CitiGroup in a little over two decades, as seen in this 2010 graphic from Mother Jones.
CORRECTION: A previous version of this post misidentified General Mills as the parent company of Corn Flakes and Frosted Flakes. See what the 10 companies own in the infographic below:

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These 10 Companies Control Basically Everything You Consume [GRAPHIC]

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It may be obvious that Corn Flakes and Frosted Flakes are both made by Kellogg's, but did you know that Hot Pockets and L'Oreal share a parent company in Nestlé? A ginormous number of brands are controlled by just 10 multinationals, according to this amazing infographic from French blog Convergence Alimentaire.

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This social network image I found to be useful because it looks a lot like the ones we have made in the past. It is a little difficult to follow because there are many nodes, and the lines are not straight as Kozo suggests they should be. These nodes are also made to have a picture of the person rather than their name which could be confusing.

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I picked this picture off of google image because it is a good breakdown of multiple different websites and how they are all connected to each other. I know personally I always enjoy when I can find things on Facebook and the link to Pinterest is there and I can easily pin it to my Pinterest. The lines are definitely easy to follow, are straight and the nodes that are connected are close to each other. I think that this is good and sensible visualization! The pictures of the websites logo also helps as nodes.

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This network has a majority of straight lines, however, they consistently cross over one another.  They certainly are not far apart from one another. This makes the ties very difficult to follow. It is also difficult to determine which node is the most central as they are arranged in a cluster. I do not think that this is a sensible visualization and is very confusing because it is so complex. There are SO many ties and nodes. And because it seems that most of the nodes are connected to multiple nodes, the lines crossing over make it difficult to follow.

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https://griffsgraphs.files.wordpress.com/2012/07/basketball_players_label_lowres.jpg
Lot of straight lines when zooming in.  Lines are pretty close to each other, cross, and I think hard to follow.  Connecting nodes are all over.  It appears similar nodes are close to one another.
This graph is connecting basketball players.  The nodes are clustered by current teams.  They are connected if they played with the other athlete on the same team at any one time.  I don't see what the benefit could be to this graph.  Players that have been on many teams could mean something to someone.  Node size is helpful.  Lot of lines is intimidating.
The author has other graphs if anyone is interested, on his site.

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I found this image on google and I choose to include it because it was much simpler and easier to understand then most of the other network graphs available. Starting off with the first of Kozo Sugiyama's guidelines it doesn't have completely straight lines, they have a slight curve to them. I think it follows the second guideline that says the lines should be far apart, they seem to have a good equal distance between them. The lines do cross or touch in some places so guideline three doesn't fit. The lines are easy to follow, and connecting nodes are fairly close to one another. It seems like there isn't one most central node, they are all just evenly spaced out. And the network graph seems to follow the last guideline as well. I find this to be a good visualization, the only thing I dislike about it is that there are quote bubbles over all of the people and it gets in the way of some of the lines. There should be a place to write in names under the person instead.