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During Microsoft Dynamics Convergence in Atlanta in March 2014, Microsoft unveiled a great deal of exciting new products, soon to come features and new licensing options for Microsoft Dynamics CRM Online. A brand new, end-to-end marketing automation solution including campaign management, email marketing, lead nurturing, marketing list management, assets and media management, marketing budgeting, marketing analytics, ROI and reporting, and much more. A new CRM feature providing powerful insights into social media channels like Facebook, Twitter and LinkedIn, and allowing marketers and sales reps alike to track products, brands, competitors and marketing campaigns across the social web. With the acquisition of Parature, Microsoft brings enhanced customer service capabilities to Dynamics CRM, including features such as a self-service knowledgebase, live chat, case management, social care and a unified service desk. Along with the many rollouts of new products and functionality, Microsoft is introducing a new SKU called Microsoft Dynamics CRM Online Enterprise. The chart below provides a high level overview of the new features, product enhancements and licensing for the 2014 spring release of Microsoft Dynamics CRM Online. With the many new products and feature enhancements to be rolled out with the CRM 2014 spring release, Microsoft is introducing new licensing options for Microsoft Dynamics CRM Online. Microsoft Dynamics CRM Online Enterprise is a brand new SKU introduced with the 2014 spring release. Social Media Click on the social media icons below and stay connected with us across the web and throughout your day.
Enter your name and email address to receive monthly updates about our latest news, events and free technology resources for your business! One of the cool things about the Social Dynamics and Network Analytics (Social-DNA) course that I teach at Kellogg is that there are lots of new research articles, news stories, magazine articles, and blog entries coming out all of the time that are relevant to the course content. To help facilitate conversation about these current events, this quarter we're introducing #socialDNA on Twitter.
I was surprised this week to find an article on Big Data in the New York Times Men's Fashion Edition of the Style Magazine. The Wisdom of Crowds is the idea that, as collectives, groups of people can make more accurate forecasts or come up with better solutions to problems than the individuals in them could on their own. This is a perfect example of the Wisdom of Crowds: by pooling the forecasts of a diverse group, they came up with an accurate collective forecast. So, Sorkin is on the right track to write a great article on how sample bias is still important even when you have Big Data. The scientific evidence shows that using crowds is a pretty good way to make a prediction, and it often outperforms forecasts based on experts or Big Data. The reason the Wisdom of Crowds works is because when some people forecast too high and others forecast too low, their errors cancel out and bring the average closer to the truth. Spechler and Cannarella's paper predicting the death of Facebook has been taking a lot of flak. The most common complaint about the Princeton Facebook paper that I've seen is that Facebook is not a disease. Facebook may not be a disease, but that doesn't mean a model that describes how diseases spread isn't a good model for how Facebook spreads. Interestingly, one of the major points of Spechler and Cannarela's paper is that online social networks do NOT spread just like a disease, that's why they had to modify the original SIR disease model in the first place. But, the critics have missed this point and are fixated on particulars of the disease analogy. Spechler and Cannarela fit their model to data on MySpace users (more specifically, Google searches for MySpace), and the model fits pretty well.
This morning my email is blowing up with links to articles describing research by Joshua Spechler and John Cannarella, two Princeton PhD students, that predicts Facebook will lose 80% of its user base between 2015 and 2017. The paper is getting plenty of criticism, but as far as I can tell most of the critics haven't read or didn't understand the math in the paper.
Spechler and Cannarella update this model by making the recovery rate proportional to the number of recovered individuals. The effect of adding this loop is that recovery is slower in the beginning, because few people have recovered so there isn't much social pressure to recover, but then to accelerate recovery as the recovered population grows.
To test their model they fit it to data on MySpace (they use Google Search data, which is a cool idea) and find that it fits pretty well. More problematic is that the model doesn't account for what is most likely the biggest single reason that people left MySpace — Facebook. It's quite possible that Facebook will pass away, but probably only if something new comes along to displace it, not because people are embarrassed if someone finds out they still have an account.
The article cites research by Jonah Berger and Katherine Milkman that finds articles with more emotional content, especially positive emotional content, are more likely to spread. The second issue has to do with the overall predictability of making the most emailed list. Last week the website of The Atlantic had a nice network visualization of the top tweets linking to articles on gun politics.
The edges in the network visualization connect two pages if the same Twitter account posted links to both pages. In this network the nodes are blogs about politics and two blogs are connected if there is a hyperlink from one blog to another. Or, take a look at this network of senators created by a group in the Human-computer Interaction Lab at the University of Maryland.
Here, the nodes are senators and two senators are connected if they voted the same way on a threshold number of roll call votes. Here the nodes are students in a high school and two nodes are connected if one student named the other student as friend (the data was collected as part of the Add Health study).
To understand the difference it helps to think about other reasons why we might see similar nodes preferentially connected to one another. In the network dynamics story, nodes form or break ties in a way that shows a preference for a particular attribute. But, just because we see observed homophily doesn't mean people are preferentially linking to other people that are like them.
Students in my Kellogg MBA and EMBA Social Dynamics and Networks classes do a lot of work using the network analysis and visualization software package Gephi.
To get started, follow steps 0 and 1 here to setup a Twitter account and download the NodeXL software.
As discussed in the previous post, because of Twitter rate limits, it is advisable to limit your request to a fixed number of people.
Once you download the data using NodeXL, I like to export it as a graphml file and then visualize it in Gephi. Before getting started with manipulating the network in Gephi, it is a good idea to go into the Data Laboratory and delete some of the columns that NodeXL created. First, I filtered out all of the accounts except those that belong to the largest connected component of the network. NodeXL is a freely available Excel template that makes it super easier to collect Twitter network data.
Limit to XXX people — This is a key variable to set and really depends on your level of patience (see Warning: Twitter Rate Limiting below). Twitter limits the number of times per hour fifteen minutes that you can query the API (Application Programming Interface).
Once you have the data, you can either analyze it within NodeXL or export it to analyze using another program. Over the past year I was following up and keep myself updated regarding the new version of Microsoft Dynamics AX, recently Microsoft have launched Microsoft Dynamics AX 2012.
Second changes are in logical distribution of business functions among modules, which gives control which complying the basis of segregation of duties at end user level, in addition to new modules which serve important business functions. There are some functions have been separated to another modules such as AP in Microsoft Dynamics AX 2009 was covering AP – Financial activities, and Purchasing activities, in Microsoft Dynamics AX 2012 AP is covering Financial activities, and Procurement module is covering Procurement activities.
The social tab is a great spacing saving component because it uses tabs, so you can quickly move between activities, posts and notes.
The social tab works pretty well, allowing the user to quickly view and create activities, posts.
The social tab was initially created in CRM 2013 and people liked it but got frustrated because they couldn’t customize the social pane.
Any suggestions as to how I can reduce the depth of the Conversations Tab in the case form? Get help on your Microsoft Dynamics CRM solution from qualified experts in the forums, read blogs and how-to articles, and watch videos.
Today on Slate there is a nice little GIF (that originally appeared on The Atlantic) that shows how obesity rates have changed over time by state.
As a side note, it's interesting to compare the map of the "obesity epidemic" to a map of something we know spreads through person to person contagion, like the swine flu (image from the New York Times).
Unlike the obesity epidemic, swine flu jumps all over the place, which obviously has to do with air travel. Along with a team of researchers led by epidemiologist David Shoham from Loyola University, I recently published a paper in PLoS One examining the social contagion of obesity. One alternative approach is to use a "generative model," which is exactly what my coauthors and I do.
In our paper, we apply the model to data from two high schools from the AddHeath study. We use the model to understand social influences on body size, physical activity, and "screen time" (time spent watching TV, playing video games, or on the computer). In both schools students are more likely to select friends that have a similar BMI (body mass index), that is there is homophily on BMI. There is no evidence for homophily on screen time in either school, and there is evidence that students are subject to influence from their friends'  on screen time in only one of the two schools.
In one of the two schools there was evidence for homophily on playing sports, but in both schools there was evidence that students influenced their friends when it comes to playing sports. Anyone interested in applying tools from economics to studying networks or tools from network science to studying economics is invited to the satellite symposium on Economics in Networks to be held in conjunction with NetSci 2012 at Northwestern University in Evanston on Tuesday, June 19.
While I've been teaching Social Dynamics and Networks at Kellogg, I've amassed a collection of links to interesting videos on social dynamics.
There are several good videos of talks from the Web Science Meets Network Science conference at Northwestern: Duncan Watts, Albert-Laszlo Barabasi, Jure Leskovec, and Sinan Aral. The "Did You Know?" series of videos has some incredible information about, well, information. I would recommend this course to anyone who isn't on the verge of finding a cure for cancer or solving world hunger, because otherwise whatever your doing probably won't be more impactful than taking this course. I take a much more complicated route to the same conclusion in "Network Games with Local Correlation and Clustering." Besides being sufficiently mathematically intractable to, hopefully, be published, the paper does show a few other things too. This new package includes Microsoft Dynamics CRM Online Professional, Dynamics Marketing, Social Listening and the customer service features available in Parature. The 2014 release of CRM Online Professional brings additional functionality to the package in the form of Microsoft Social Listening – at no additional cost (please note that a minimum of ten users is required to get Social Listening for free). The package comprises Dynamics CRM Online Professional, Dynamics Marketing, Parature and Microsoft Social Listening. This option does not include Dynamics CRM licensing, Microsoft Social Listening or Parature.
Anytime you come across something relevant to the course topics (which you can read more about here), tweet about it with the hashtag #socialDNA. A different recent New York Times articles has some great examples of the Wisdom of Crowds.
For example, looking at presidential elections from 1988 to 2004, relatively small Wisdom of Crowds forecasts outperformed the massive Gallup Poll by .3 percentage points (Wolfers and Zitzewitz, 2006).


The accuracy of a crowd forecast depends both on the accuracy of the individuals in the crowd and on their diversity — how likely are their errors to be in opposite directions. While I do think there are some issues applying their model to Facebook and MySpace, they're not the ones that most people are citing. For example, Lance Ulanoff at Mashable (who has one of the more evenhanded critiques) says, "How can you recover from a disease you never had?" He's referring to the fact that in Spechler and Cannarella's model, some people start off in the Recovered population before they've ever been infected. The first method is easier, but it only tests the predictive power of the model, not how well it actually matches reality. The model has several parameters — most importantly there is an "infectivity" parameter (? in the paper) and a recovery parameter (?). Even for diseases, which are much simpler than social contagions, it takes lab experiments and lots of observation to estimate these parameters. Imagine everyone in the world is in one of three states, Susceptible, Infected, or Recovered. When I was in junior high, it was cool to "tight roll" your jeans as demonstrated by these ladies. A quick read of the article seems to promise an easy path to getting your own content on your blog, YouTube, or Twitter to take off. They find, for example, that "a one standard deviation increase in the amount of anger an article evokes increases the odds that it will make the most e-mailed list by 34%."  In this case, the base rate is the percent of articles overall that make the most emailed list.
In Ohio, 37% of registered voters are registered as Republicans and 36% are registered as Democrats.
The creator of the network visualization collected all of the tweets that mentioned terms like "gun rights," "gun control," "gun laws," etc. The point is that we see two very distinct clusters with lots of edges within the clusters and not too many between them.
For example, take a look at this network of high school friendships from James Moody's paper "Race, school integration, and friendship segregation in America," American Journal of Sociology 107, 679-716 (2001). The casual stories fall into three basic categories: influence, network dynamics, and exogenous covariates.
Our intuition is that liberal blogs like to link to other liberal blogs more than they like to link to conservative blogs. This is reassuring when we see homophily on dimensions like race as in the high school friendship network above. In this case, we can rule out influence, since the articles had to already exist and have a stance on gun control before someone can tweet a link to them.
When I try to export my graph as a pdf, Gephi clips the node labels so that I can't see all of them. I imported a graphml data file and I'm trying to use eignevector centrality (PageRank, HITS, …) to identify important nodes, but when I try to run the eigenvector centrality calculation from the Statistics window nothing happens. The problem is that the graphml file that you imported already has (empty) columns corresponding to the measures that you want to calculate and Gephi won't overwrite them. I imported a node attribute that I want to use to resize my nodes, but it isn't showing up under the ranking tab. The most likely problem is that the node attribute is identified as the wrong type of data — probably a String, when it needs to be a numeric type such as BigInteger. For some reason, when importing an adjacency matrix Gephi expects a csv file with semicolon separators, not commas. Be sure these are the same names that appear under the ID column in the Data Laboratory in Gephi.
Make sure that when you're importing the edge list from the data laboratory that you select  "Edges Table" in the drop down menu and not "Nodes Table." Otherwise it just thinks your bringing in a list of nodes.
I want to add labels to my network, but when I click the little black T, no labels show up (or the label isn't what I want it to be). When I try and import an edge list, Gephi says I need Source and Target columns, but I already have Source and Target columns.
Then, to download the network data, click on Import and select From Twitter Search Network… In the first dialog box, enter the search term that you want to look for. In this example, I did a few things to make the visualization more meaningful, which I describe below.
You should delete anything having to do with the color or size of the nodes or edges, or centrality measures such as PageRank and eigenvector centrality.
This makes the network much more readable, and allows us to focus only on those nodes involved in a large cascade. Besides the fact that having an account will make getting data faster, it’s good for you to have a little Twitter experience before you dive into the exercise. Besides downloading the data, you can also use NodeXL to visualize and analyze network data, but I prefer to export the data and use another program like Gephi to do the visualization and analysis.
We’re going to collect data on people that follow a person, company, or product, or if you want you can use yourself (this will only be interesting if you have a healthy Twitter presence).
You’ll want to authorize NodeXL to access your Twitter account by selecting the radio button at the bottom and following the onscreen instructions. For example, if you want to analyze the data using Gephi, click on Export and choose the GraphML format. Click Sign In to add the tip, solution, correction or comment that will help other users.Report inappropriate content using these instructions. If you are Functional Consultant used to the previous version Microsoft Dynamics AX 2009 you will find very interesting changes in this release Microsoft Dynamics AX 2012, that will give you more power in the implementation phases, in addition to customer adoption to Microsoft Dynamics AX 2012. First changes are in the interface which has been changed to be simpler and user friendly at all levels. Slate seems to suggest that the geographic progression of obesity rates might indicate some sort of social contagion. For example, income might affect the types of stores and restaurants available, which in turn affects obesity rates. Page from the University of Michigan and Santa Fe Institute and he pointed me to an amazing opportunity. Scott is a fantastic teacher — I have been lucky to co-teach course with him at the University of Michigan and in the ICPSR summer program. In particular, I look at how network clustering affects "public goods provision," which is the fancy term for what Jeff Ely calls subscribing to the newspaper. Additional fees may apply for add-on services such as additional storage, additional test instances, additional email messaging etc. We're asking all of the current students to tweet a #socialDNA tweet at least once during the quarter (just retweeting someone else's #socialDNA tweet doesn't count!) We also hope that former Social DNA students will get involved too and this will provide a way for alumni of the course to stay connected with the latest Social Dynamics research and current events.
The central example in Sorkin's article is data from Twitter, and in particular data that showed a lot of people on Twitter were very unhappy with antigay comments made by Phil Robertson, the star of A&E's Duck Dynasty. But unfortunately the article veers off that track when it starts talking about the Wisdom of Crowds.
This isn't a huge margin, but keep in mind that the Gallup presidential poles are among the most expensive, sophisticated polling operations in history, so the fact that the crowd forecasts are even in the ballpark, let alone better, is pretty significant. The great thing about it is that you can make up for low accuracy with high diversity, so even crowds in which the individual members are not that great on their own can make pretty good predictions as collectives. It just means that by changing the model parameters, we can make the adoption curve in the model match the same shape as the adoption curve in the data. For this test, Spechler and Cannarela could fit the model to data from the first few years of MySpace data, say from 2004 to 2007, and see how well it predicts MySpace's future decline.
These parameters could be estimated directly by estimating how often people contact each other with messages about these social networks and how likely it is for any given message to result in someone either adopting or disadopting use of the network.
Spechler and Cannarella's starting point is a basic model of disease spread called the SIR model. The SIR model (and its marketing variant the Bass model) have been applied to study the spread of innovations for decades.
Every time a Susceptible person bumps into an Infected person, there is a chance they become Infected too. In terms of Facebook, this would be interpreted as an increasing social pressure to leave Facebook as more other people leave Facebook.
When Specheler and Cannarella fit this model to the data, the best fit predicts that this mass exodus for Facebook will occur between 2015 and 2017. First, just because the model fits the data well doesn't mean that the model captures what's really happening. But in reality, people probably didn't feel pressure to not be on MySpace, they left MySpace because they felt pressure to be on Facebook because that's where everyone else was. For example, the article cites Gawker editor Neetzan Zimmerman's success, pointing out his posts generate about 30 million views per month — the kind of statistics that get marketers salivating. When we hear that writing an especially angry article makes it 34% more likely to get on the most emailed list, it sounds like angry articles have a really good chance of being shared, but this isn't necessarily the case. This model has more than 20 variables including things like article word count, topic, and where the article appeared on the webpage.
Of course, taking a loser look at the network visualization we see that one of the groups consists of pro gun control articles and the other contains anti gun control pages. As we can see, "yellow" students are much more likely to be friends with other yellow students and "green" students are more likely to connect to other green students.
Clearly, the students are not influencing the race of their friends, but this doesn't mean the fact that we observe racial homophily doesn't imply the students are racist — there could be what we call an exogenous covariate that is leading to the observation of homophily.
That is, the "state" of the node as pro or anti gun control precedes the formation of a tie connecting them in the Atlantic's network. I thought it would be helpful to compile a Gephi FAQ, so I scanned through my old emails looking for Gephi questions and have posted some of the most common ones here along with their answers. When Gephi exports the image, it only pays attention to nodes and links, not the labels, when it decides where to clip the image. The easiest way to fix this is to click Duplicate column in the data laboratory and then be sure to select a numeric type (e.g. Then add additional columns to the spreadsheet that give the node attributes for each node. By default, Gephi uses the data in the column "Labels." To change which column is used, from the over view screen, click the small triangle in the lower right hand corner of the Graph window, which will reveal an extra settings pane. Any account that recently posted a tweet containing this phrase will end up being a node in your network. These columns are generally empty, but unless you delete them, Gephi won't overwrite them when you ask it to calculate these measures, so you won't be able to calculate and make use of them in your analysis.
After trying a few options, I choose the Force Atlas layout algorithm to arrange the nodes. Once your account is authorized, you should find a company or product on Twitter that you’re interested in (you can do this inside Twitter via a browser).
This can quickly put you up against the rate limit and you will have to wait an hour before any more data can be downloaded. But, ss many others (and here) before me have pointed out, we have to be very careful when trying to draw inferences about social contagion. If we take a look at a map of household income by state, we see that there is a lot of overlap between the poorest states and those with the highest obesity rates.
For a more careful look at some data on the social contagion of obesity, have a look at our paper that examines obesity rates, screen time, and social networks in adolescents. Essentially, this model assumes that people make choices about their friendships and behavior just like economists and marketers assume people make choices about where to live or what car to buy.


He's the author of The Difference, Diversity and Complexity, and Complex Adaptive Systems: An Introduction to Computational Models of Social Life. In my research, I use modeling to try and understand how social interactions give rise to this astounding diversity of phenomena.
Unfortunately, the article by Andrew Ross Sorkin (author of Too Big To Fail) made one crucial mistake. The Wisdom of Crowds is not about combining data on large groups, but about combining the predictions, forecasts, or ideas of groups (they don't even have to be that large).
In fact, as long as some of the individual predictions are on both sides of the true answer, the crowd forecasts will always be closer to the truth than the average individual in the crowd. Frank Bass's original paper has been cited thousands of times and was named one of the ten most influential papers in Management Science. While it's received its fair share of criticism, the entirety of The Tipping Point is based on the disease spread analogy. It is a bit confusing that they're referred to as "recovered" in the paper, but if we just called them "people not using Facebook that never will in the future" that would solve the issue. Since both go up and then down, and there are enough model parameters so that we can change the speed of the up and down fairly precisely, it's not surprising that there are parameter values for which the two curves match pretty well. Without calling it by its name, I discussed applying the SIR model to the spread of memes online in the previous post on "What it Takes to Go Viral". Once a person is Infected, they stay Infected for awhile, but eventually they get better and become Recovered.
It just means that you can manipulate the parameters of the model to produce a curve that goes up and down with a shape similar to the up and down curve that describes the users of MySpace over time. The scientific research by Berger and Milkman is interesting and well done, but we have to be careful about how far we take the conclusions. But even knowing all of these variables, we still can't accurately predict if an article will make the most emailed list or not. On average, registered voters in Missouri are more likely to be Republican than registered voters in Ohio, but just because someone is from Missouri doesn't mean we can confidently say they're a Republican. The problem is so deeply ingrained in the concept of homophily that it sometimes leads to ambiguity in the use of the term itself.
In this explanation, we imagine that the network of connections already exists, and then nodes that are connected to one another affect each other's characteristics so that network neighbors end up being similar to one another.
For example, it could be that these students leave in a racially segregated city and students are more likely to be friends with other students that live close to them. To make sure you get the full image, you can add some nodes around the edges of where you want to clip the image. Go into the data laboratory tab and delete any of the columns that have to do with measures of centrality like eigenvector centrality, closeness centrality, betweeness centrality, page rank, anything that looks like that. For example, you might have a column called "type" with entries like "doctor" or "patient" that tells whether the corresponding node is a doctor or a patient. Once you have created your spreadsheet, export it as a csv. NodeXL will automatically pause when you reach the Twitter rate limit and wait for an hour to begin downloading data again. If you have time to let your computer run all night (or for several days), then you can increase the limit to more people. This means that if I'm friends with Jack and Jill, there is a good chance that Jack and Jill are friends with each other.
The blog is a place for me to make more casual observations about social dynamics in the news, in my research, in other people's research, and in everyday life. The downfall of the article was conflating two distinct concepts that are both near and dear to my research, Big Data and the Wisdom of Crowds, which led to a completely wrong conclusion. So, if you're used to dealing with data in Excel on a laptop, then data that needs a small server and some more sophisticated analytics software is Big for you.
If you want to use the Wisdom of Crowds to predict an election winner, you don't collect data on who they're tweeting about, you ask them who they think is going to win. Ulanoff has the same sort of quibble with the term recovery writing, "The impulse to leave a social network probably does spread like a virus.
They measure how infectious a disease is and how long ti takes for someone to recover, on average.
Suppose for example that only 1 out of every 1000 articles makes the most emailed list, then what the result says is that 1.34 out of every thousand angry articles makes the most emailed list. All we know is that on average articles with some features are more likely to make the list than articles with other features.
If we only looked at people from Ohio and Missouri, knowing which state a person is from wouldn't be a very good predictor of their party affiliation. Since tweets are limited to 140 characters or less, when most people post a link on Twitter they shorten the URL using a service like bit.ly.
Some people use the word homophily to refer to the observation that nodes in a network are more likely to connect to similar nodes in the network than we would expect due to chance. For example, in a series of papers looking at a network of friends, relatives, and geographic neighbors from the Framingham Heart study, Christakis and Fowler argue that network neighbors influence one another's weight, tendency to smoke, likelihood to divorce, and depression. While not everyone is convinced by Christakis and Fowler's evidence for a contagion effect, we can all agree that in their data obese people are more likely to be connected to other obese people, smokers tend to be friends with smokers, people that divorce are more likely to be connected to others that divorce, and depressed folks are more likely to be connected to other depressed people than we would expect due to chance.
In this case, students prefer to be friends with other students that live near them, and living near one another just happens to increase the likelihood that the students share the same race. Then drag the "Component ID (Node)" filter down to the Queries window where it says "Drag filter here". To do this, there is a tool on the left side of the overview window that looks like a pencil (the top one of the two pencils).
On the far right hand side of this window, click "Configure…" then put a check mark next to any of the attributes that you would like to show up as labels. Usually, I have to increase the repulsion strength from the default setting of 200 to 2000 or more.
If you're used to dealing with data on a server, then your Big might be data that needs a room full of servers. Using a big dataset does not mean that you don't have to worry about having a biased sample. If you want to use the Wisdom of Crowds to decide whether or not you should fire Phil Robertson, you ask them, "Do you think A&E will be more profitable if they fire Phil Robertson or not?" As angry as all of those tweets were, many of those angry voices on Twitter would probably concede that Robertson's remarks wouldn't damage the show's standing with its core audience.
Sorkin concludes his article, based on the examples of inaccurate predictions from Big Data with biased samples, by writing, "A crowd may be wise, but ultimately, the crowd is no wiser than the individuals in it." But this is exactly backwards. In the process of simplification we leave things out that matter, but we try to make sure that we leave the most important stuff in, so that the model is still useful. Put these two parameters together with how often people come into contact (where the definition of "contact" depends on the disease — what counts as a contact for the flu is different from HIV, for example), and you can predict how quickly a disease is likely to spread or die out.
But for any particular article, we really can't do a very good job of predicting what's going to happen.
This means that two people that are ultimately linking to the same article might post different URLs. In this case, there is no mention of the underlying reason why similar nodes are connected to one another, just that they are. If I read news on the website of MSNBC and you go to the Fox website, I'm more likely to tweet links to pro gun control articles and your more likely to to tweet anti gun control links, even if we are both just tweeting links to every gun control article we read. You can select which component(s) you want to see by clicking on the check boxes next to the component numbers where it says "Partition (Component ID) Settings" You can see what fraction of the nodes belong to each component as a percentage next to each component number, so if you only want to see the largest connected component, chose the one with the highest percentage. If the little window pops up with the graphs, then everything is working, if it doesn't then you need to go back to the data laboratory and delete some more columns. Alternatively, in the Data Laboratory, you can just copy the column that you want to use as labels in to the labels column. Then I resized the nodes according to their degree so we can get a sense for who the most important nodes in the network are. He is a super clear thinker and has an infectious enthusiasm for modeling and complex systems. A more accurate statement would be, "A crowd may or may not be wise, but ultimately, it's always at least as wise as the individuals in it. Many news services have a built in "Tweet this" button, which may give the same shortened URL to everyone who clicks it, so those articles would get many consistent links, where articles or posts without a "Tweet this" button might have many links pointing to them, but all with different URLs coming from each time a person shortened the link individually.
Undoubtedly though, many people are using Twitter as a way to spread information that supports their own political opinions, so someone that is pro gun control will tweet pro gun control links and vice versa. Put one node on the left, right, top and bottom at the boundary of where you want the image to be clipped.
A classic example is the prediction by The Literary Digest in 1936 that Alf Landon would be the overwhelming winner of the presidential election that year. For example, take a look at this network of political blogs created by Lada Adamic and Natalie Glance (they have generously made the data available here). In lay terms, triadic closure means that two people with a friend in common are likely to be friends with each other — the triangle closes instead of remaining an open like a V. This however doesn't mean that gun control advocates aren't reading 2nd amendment arguments and gun rights supporters aren't reading what the gun control folks have to say — it just means that they aren't broadcasting it to the rest of the world when they do.
Then, so these nodes don't actually show up, you can resize them so that they're so small that they can't be seen.
No map completely reproduces the land it represents, and different maps focus on different features. It could be that, in the high school friendship network, there is a sight tendency for some students to choose others of their same race as friends; either because of another variable like location or because of an actual racial bias, but the appearance of racial homophily could be significantly amplified by triadic closure. To do this, select the sizing tool, which looks like a little diamond on the left side of the overview window. This should add a new column to the nodes table in the data laboratory. Then, using the partition tab, you can color the nodes according to this attribute. The prediction was based on a huge poll with 2.4 million respondents, but the problem with the prediction was that the sample for the poll drew primarily on Literary Digest subscribers, automobile and telephone owners.
If one student chooses two friends that are of the same race, triadic closure is likely to result in third same race tie.
Then click on the node that you want to reize and drag the mouse down to make the node smaller. I colored the nodes according to which community they belong to as identified using Gephi's implementation of the Girvan-Newman modularity based clustering algorithm, and I colored the edges according to the type of relationship between the Twitter accounts. This sample tended to be more affluent than the average voter, and thus favored Landon's less progressive policies. One kind is good for hiking the Appalachian trail, another is good for navigating from New York City to Boston. It turns out that, at least in some cases where scholars have been able to untangle these various stories, triadic closure and homophily on other covariates explains a lot of observed racial homophily (see e.g. Models are the same — they leave out some details and focus on others so that we can have a useful understanding of the phenomenon in question. The SIR model, and Spechler and Cannarela's extension leave out all sorts of details of disease spread and the spread of social networks, but that doesn't mean they're not useful or they can't make accurate predictions.



The secret history vol. 1
The secret service 2013
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