# data[].attributes.body
This review padlet has been closed to new comments and questions as of 11:59 pm, March 16, as noted in the lecture. Thanks for your participation! -- Prof. Cook

# data[].attributes.body
I feel like using the diagrams to understand the networks 1.0, and 1.5 are very helpful. Going through all of the notes that I have taken and looking back at the lectures and visual pictures will be very helpful for reviewing the material and understanding the material fully. However, some of the calculations I have had trouble with such as the ones in homework # 2 and the ones in Homework #5.

===

Hi, Volans. Which ones in particular? There are a lot of calculations in there, and of course I can't review them all here. What are the specific calculations with which you have trouble, and at what specific step in the calculation process? Sometimes getting down to a specific question is actually a good way of figuring out the answer. -- Best, Prof. Cook

# data[].attributes.body
So far I have only done poorly on the "line for heaven" homework assignment. I feel silly that I am not understanding the difference between a 1.0 and a 1.5 network. I thought that I had the edge list down. Then I entered it in the node excel and did it incorrectly because I wasn't grasping the difference between the 1.0 and the 1.5 network. I just want to make sure I understand this becuase I feel it is pretty basic.

===

Asterope, thanks for writing. The difference between a Level 1.0 and Level 1.5 ego network is indeed pretty basic, but it's okay -- it's human that we sometimes get hung up on basic differences. (Myself, I can never remember which way to screw a screw tighter - left or right? ) Let's cover it again, using slightly different language:

In a LEVEL 1.0 ego network, the nodes that are included are ego and ego's alters. The ties that are included are only the ties from ego to ego's alters. At this level, ties between ego's alters are NOT included.

In a LEVEL 1.5 ego network, the nodes that are included are ego and ego's alters. The ties that are included are the same ties that are included in a Level 1.0 ego network, plus ties between the alters of ego.

Does that help? -- Best, Prof. Cook

Asterope, I had a similar issue with the Line to Heaven HW, my big problem was that I thought there was no 1.5 ego network because not of my alters had ties to each other. As it turns out there was a 1.5 ego network, it was just identical to the 1.0 network. Sometimes the simplest things can be the most confusing.

Leo

Thank you both!!! Yes, the simplest things can definitely be the most confusing at times. I get frustrated when I think I understand something and find out that I had it totally wrong! That makes much more sense and thank you both for responding.

# data[].attributes.body
I"m getting what seems like the right answers for the friends have more friends, but I'm getting them backward from the teaching. A hard long way around. Is there a simple equation that I'm missing so I can work forward and not backward?

===

Thanks for writing -- but to be honest, Columba, I'm not sure what you mean by "getting the right answers" but "getting them backward from the teaching." Do you mean you're using another method for getting the answer? A key to getting the answer in relatively short fashion has been shared not only by Feld in his Table 1 -- follow his example -- but also as a link I provided below in this same padlet. I'll embed the link again here:

# data[].attributes.attachment_link.readable_text
In Networks Lecture 8, I presented you with a social network in which toys are nodes, then asked you to show that on average in the network, a node's friends tends to have more friends than the node itself has. This exercise is inspired by sociologist Scott Feld's paper, " Why Your Friends Have More Friends Than You Do."

For those who aren't sure how to go about it, the following is a table of calculations, following the example of Feld's Table 1, that demonstrates the pattern:

# data[].attributes.attachment_link.title
A Toy's Friends Have More Toy Friends Than the Toy Does

# data[].attributes.attachment_link.description
In Networks Lecture 8, I presented you with a social network in which toys are nodes, then asked you to show that on average in the network, a node's friends tends to have more friends than the node itself has. This exercise is inspired by sociologist Scott Feld's paper, " Why Your Friends Have More Friends Than You Do."

# data[].attributes.body
I have pretty much a grasp on everything with the exception of some of the calculations.  So I would like a refresher in regards to the diameter and density of a network.  I really struggled with that on homework 2.  I thought after that particular assignment I would be set but I still find myself struggling with those two for some reason.  Thanks in advance!

=====

I also struggled with diameter and density calculations , especially with homework 2. A refresher would be very helpful. Homophily was one thing that I think I have grasped, however, I wasn't sure if induced homophily would be the same as geographical homophily or if they are distinct from each other? Because in theory if you are limited to geographical location and proximity then this could be defined as induced because of the area in which one lives and works. -LIBRA

=====

Hi, Polaris and Libra. Diameter is simple: it is simply the longest geodesic distance between any two nodes in a network. Take a look at all the shortest paths (geodesics) between nodes in a network and find the longest one. Note how long it is. That's diameter.

As for density, what step are you having trouble with? I won't repeat myself or Prell, because that wouldn't be helpful, but if you go through the steps of calculating density, what is the step at which you find yourself going wrong?

Always ask the most specific question possible to get the best answer, because the answer you won't like is "go read THIS." But, since you have asked a general question saying you just don't understand an idea, I don't have much to give you except to embed a discussion of density from the glossary I've written in this website. Check it out and see if that helps. -- Best, Prof. Cook

# data[].attributes.attachment_link.readable_text
[Glossary Index]

The concept of the density of a social network is simple: it is a description of the proportion of possible ties in the network that are actually present. In the abstract, it is measured as a fraction: (the number of actual ties in a network) divided by (the number of possible ties in a network). Because the number of actual ties can never go below zero and can never exceed the number of possible ties, the result varies between 0 and 1. When multiplied by 100, it represents the "percentage of all possible ties in a network that are actually present" ( Christina Prell 2011: p. 120).

How that fraction is actually measured depends on the sort of network one is measuring and what sort of ties the network contains.

Number of Actual Ties
In a complete network, the number of actual ties is measured literally, as a count of the actual ties present in the network. But in an ego network, by convention the "number of actual ties" counted does not include ties involving ego. This is because ties involving ego's relation to alters is there necessarily as part of the definition of what an alter is.

Number of Possible Ties
In a directed network, the number of possible ties is calculated as n*(n-1). In an undirected network, the number of possible ties is calculated as (n*(n-1))/2. When calculating density in a complete network n refers to the number of nodes. When calculating density in an ego network n refers to the number of nodes that are not ego.

# data[].attributes.attachment_link.description
Glossary Index ] The concept of the density of a social network is simple: it is a description of the proportion of possible ties in the network that are actually present. In the abstract, it is measured as a fraction: (the number of actual ties in a network) divided by (the number of possible ties in a network).

# data[].attributes.body
I think searching through readings and the material is demonstrating that I think I have a grasp on most concepts. One thing that is stumping me is with the assignment where we made matrices of events/Congressmen. Say a person was in three events, he would be considered to be in these events with himself... so there would be a 3 present in box where it normally would have no value... Why does it matter in this case and not the other matrices before? Is it because its a two-mode matrix?

===

Lynx, Aquila here. One mode and two mode matrices were something that gave me a little trouble too, so maybe we can figure this out together. If a congressman/woman was in multiple events (committees) you would only give them a one in each column, a two mode matrix contains data about a node and an event, or you could describe a two mode matrix as having non corresponding data. A one mode matrix has corresponding data (senatorsx senators, like in our homework for example.) You give values of more than one only in matrixes with corresponding data, it would be unnessecary to give a value of more than one in a two mode network, because you would be able to visualize that a senator is in more than one committee/event by looking at the matrix. Hope I could explain this. I felt that the one and two mode matrix's were the part I had the most trouble understanding.

===

Lynx, this is Prof. Cook. Remember that the cells in a 1-mode matrix referring to the relationship between a node and itself are (taken all together) called the diagonal. The diagonal cells in a 1-mode adjacency matrix for a relation like "eating lunch with" are meaningless because knowing that Sam Johnson eats lunch with himself is meaningless. Of course he does -- he couldn't do anything else but always eat lunch with himself! So in that case we pretty much ignore the diagonal.

On the other hand, the diagonal cells in a 1-mode adjacency matrix for a relation like "the number of organizations one node shares with another" are meaningful, at least a little, because if we know that Sam Johnson shares five organizational memberships with himself, that's another way of saying that Sam Johnson is a member of five organizations. Because it tells us something in this case, we pay a little attention to the diagonal in this case. That's all.

Two-mode matrices are tough to think about -- but they matter. Two-mode matrix information is used in government, in activism and in business to a striking degree, as we'll find out in upcoming weeks, to find out what people are doing... and even to shape human behavior to some extent.

# data[].attributes.attachment_link.readable_text
[Glossary Index]

The diagonal of a matrix is the set of cells in the matrix that refer to themselves. The diagonal of an adjacency matrix representing social ties is important because we usually do not think of people as being able to relate socially to themselves (it doesn't make sense to say that someone went out on a date with themselves, or gave themselves syphilis, or tells themselves the latest gossip). For that reason, the diagonal of a 1-mode social network is often left blank to represent its meaninglessness. The diagonal is highlighted in yellow in the matrix below.

There are times when a diagonal can be meaningful. For instance, when a 1-mode matrix is generated from a 2-mode matrix to represent the group memberships shared between pairs of people, the diagonal represents the number of shared memberships a person holds in common with herself or himself. In such a case, the diagonal tells us how many groups a person is a member of.

# data[].attributes.attachment_link.description
Glossary Index ] The diagonal of a matrix is the set of cells in the matrix that refer to themselves. The diagonal of an adjacency matrix representing social ties is important because we usually do not think of people as being able to relate socially to themselves (it doesn't make sense to say that someone went out on a date with themselves, or gave themselves syphilis, or tells themselves the latest gossip).

# data[].attributes.body
Thank you Pisces for asking your question on levels. Out of all the weeks we have had, the Line to Heaven  week I struggled with explaining and demonstrating the levels. And thank you Ara for clarifying the counting levels and answering the question. I don't have a new question to post today, Pisces asked exactly what I was going to post!!

# data[].attributes.body
Pisces I'm not certain if you've told us enough about how you're counting levels to say for certain what you're getting wrong, but I'm prepared to suggest something. It sounds like you may be oversimplifying how you're thinking about levels. The number of ties between the ego nod, and that third node are part of how you identify a level, but they're only part of it. The key is in how ego networks talk about "alters." A Level 1.5 ego network can have connections that go from "node ego" to "node alter" to a third "node alter" without going through ego. That doesn't make it a level 2.0 ego network because that third node is connected to the ego directly as well as through an alter. For a level 2 ego network to be a level 2 that third "node alter" has to have no connection to the "node ego." The professor described those third nodes in a Level 2.0 ego network as being "only the alters of alters." They can't connect to the ego.

===

Ara, thanks for that clarification. You're absolutely on target.

- Best, Prof. Cook

# data[].attributes.body
No matter what I have done through this semester, I still get the simple things wrong. I have some of the more advanced things down but still get the level 2 ego networks and such wrong. How do we count the levels? I thought a tie going from the ego to a node was one then from that node to another was 2 and so on, but there must be a flaw to my rationale as I was often marked wrong for such thinking. Can anyone help out here?

# data[].attributes.body
I was looking for a way to determine the network size of people who participate in our needle exchange program in relationship to what types of services they utilize. I would like to use this information to project out future cost of resources needed.  I know the one mode network would be the people involved in the exchange.  Could I then set up a 2 mode matrix similar to the last couple of weeks whereby I would list the names of programs people participate in then use binary measurements to establish usage by person?  This would also give me data which identifies how many people utilize similar services and I suppose this would also begin to show me whether people become friends because of this similarity.  I am trying to apply what we have learned in a practical way at work.  Thank you in advance for your advice and clarification.

===

Aquarius, you have an excellent intuition in this regard! See my reply to your other question in the comments section below; many of the techniques for understanding sexually-transmitted diseases also apply to the study of and intervention in needle use.

But to get right to answering your question, YES! 2-mode group membership data could be used to generate a model of the 1-mode social network of intravenous drug users.

Here's something else to think of from this week's lectures: if it is true that on average a person's friends have more friends than they do, then it is ALSO true that when Person X shares needles, the people Person X shares needles with share needles with more people than Person X does! The implications of this social network principle are: 1) that sharing needles is more dangerous than a simple roll of the dice, because the people who will share needles with you are the very people who share needles with lots of people. The people you find to share needles with are the people who are most likely to be disease vectors. 2) to stop the spread of communicable disease through needle sharing, finding the most central figures in a social network (degree centrality, betweenness centrality, eigenvector centrality) and getting them to change their habits is most important.

Excellent, excellent questions this week, Aquarius!

-- Best, Prof. Cook

# data[].attributes.body
I an really interested in the network distance information that we studied this week, I am wondering what the formula would be if we took this one network distance further and if at any point does the formula drastically change to calculate distance.

===

Phoenix, the answer to your question is in the "Ripple Effect" reading. All you have to do is take your answer for the number of contacts at network distance 1 and make that your new event "e" -- then you can find the number of contacts at network distance 2. If you take that second answer as your new event, then you can find the number of contacts at network distance 3. Does that make sense? If not, after reconsulting the "Ripple" reading, please let me know and I can discuss the subject in more detail.

Best,

Prof. Cook

Professor Cook,

I went back over the ripple effect information and am much more clear about extending the network distance.

Thank you

Phoenix

# data[].attributes.body
First off, I love the Oracle of Bacon assignment, as I love Kevin Bacon. For the question of the factors that are leading to the Bacon numbers, are you looking for things like different time periods, popularity of the actor, or country of the actor's career, or something different?

====

Yes, yes, yes and yes! What are these factors? They describe ways in which the movie industry (and our society in general) segregates social networks. Good thinking -- and can anyone think of more factors like these?

-- Best, Prof. Cook

_____________________________________________________________________________________

I found that when doing this homework assignment my common Bacon number was 3, even trying to think of actors from TV Movies that never made it to DVD, or foreign films, one of the highest bacon number I got was 5 and it was a silent film actor. With that being said I somewhat found factors that would lead to higher bacon numbers, most of which Lacerta posted but what about things like age? popularity of movie genre? importance of character within a movie? how successful they are persay?

- Betelgeuse

I found the same information that Betelgeuse found for the Oracle of Bacon. Kevin Bacon is among A list celebrities so when I entered other A list celebrities I kept getting distances of 1 or 2. I am kind of behind the times when it comes to up and coming celebrities or celebrities that are not on magazines at the checkout stand in Hannaford. I had to do a little digging to find celebrities that had a further distance. I think that all of the factors that you mentioned play a role in this distinction.

Phoenix

# data[].attributes.body
My question for this week involves the homework #9 following the article "Estimating the Ripple Effect of a Disaster". I feel like this is a simple concept that I am just over thinking. My question is with the lead-in factor. How do we know what it is? Is it usually just 1.6? I used 1.6 because that is what was used in the video lecture example but I read in the article "Estimating the Ripple Effect of a Disaster" that the lead in factor is just usually greater than 1. So how would we know to use 1 versus 1.6?

Another question, does anyone have one more written example of between-ness centrality? Because that is the concept I have had the hardest time with.

===

I'll leave question #2 for others to answer, since I've provided examples of my own. Students, the best way to learn is to show someone else. Anyone want to jump in on betweenness centrality?

Your first question is certainly a fair question, Carina. Bernard et al. openly state that one might use another lead-in factor in the following passage:

"In previous work (Johnsen et al. 1995), based on General Social Survey data, we obtained lead-in factors for the populations of homicides, suicides, and AIDS victims of about 1.60, 1.26, and 1.75, respectively. The relatively low figure for suicides points to the relative social isolation and stigmatization of those who commit suicide, while the relatively high figure for AIDS reflects the relatively high social cohesiveness of those afflicted with AIDS despite stigmatization. We think that the figure for homicides reflects the fact that this is a population that is neither stigmatized nor uninteresting and that is generally not a cohesive group (though their survivors might be). We think that people who experienced the attack of September 11 are in this category, so we assume a lead-in factor of 1.60 for these populations."

So do you have a group that is "neither stigmatized nor uninteresting" but "generally not cohesive?" Go with 1.6. Do you have a socially isolated group of people participating in an event? Go with 1.26. You could change your lead-in factor based on changing characteristics of an event population. However, 1.6 is pretty safe as a middle-ground figure for a group of typical people... albeit to whom something rather untypical happened.

--Best, Prof. Cook

Professor Cook, Thank you for the feeback. Very helpful. And yes, if any other students have a grasp on betweenness centrality and could give me an example in their own words so I can look at it from some different angles that would be so helpful!

# data[].attributes.body
My question is in reference to this weeks short video with the toys that represent "Their odd friends have more odd friends than they do". At the end you asked us to pause the video, work on the 2 questions asked and then click play to see if we came up with the correct answers. I got the first question right ( the average node has 2 friends) because the majority of the toys had 2 friends each with Mr Potato Head, Red M&M and the creature that looks like he's wearing a white turban. I don't know what calculations you used to get the answer for the second question "A node's average friend has 2.76 friends". Can you please explain the calculations you used to get 2.76? Thank you.

===

Spica, see the following page -- http://www.umasocialmedia.com/socialnetworks/a-toys-friends-have-more-friends-than-it-does/ -- for an answer key with a table of calculations, following the example of Feld's Table 1. Best, Prof. Cook

# data[].attributes.attachment_link.readable_text
In Networks Lecture 8, I presented you with a social network in which toys are nodes, then asked you to show that on average in the network, a node's friends tends to have more friends than the node itself has. This exercise is inspired by sociologist Scott Feld's paper, " Why Your Friends Have More Friends Than You Do."

For those who aren't sure how to go about it, the following is a table of calculations, following the example of Feld's Table 1, that demonstrates the pattern:

# data[].attributes.attachment_link.title
A Toy's Friends Have More Toy Friends Than the Toy Does

# data[].attributes.attachment_link.description
In Networks Lecture 8, I presented you with a social network in which toys are nodes, then asked you to show that on average in the network, a node's friends tends to have more friends than the node itself has. This exercise is inspired by sociologist Scott Feld's paper, " Why Your Friends Have More Friends Than You Do."

# data[].attributes.body
Can somebody explain how to do the calculation from Moody's writing in this weeks homework?  Using my numbers, or the numbers I thought didn't work and I couldn't figure it out using the numbers provided in the example.

Thanks

---

Hi, Orion. Moody's work is a further application of "The Ripple Effect of a Disaster" by Bernard et al, and I wanted you to read it to realize how ripple effects not only affected exposure to the events of 9/11/01, but also the events afterward. This means the same basic logic by Bernard et al. works. I've gone through the example of how to make calculations using Bernard et al.'s equation -- and while it is slightly different than Moody's, it basically works for Moody's example. For that reason (and because Moody's equation is more complicated) I won't ask you to reproduce Moody's equation on the exam. Stick with the technique in "The Ripple Effect of a Disaster" and you should be fine.

Best, Prof. Cook

# data[].attributes.body
I'm with Capricorn, because the homework where we had to calculate things, my head started spinning and I had my worst homework grade of the semester. So I guess we should ask Professor Cook to respond how well we need to understand these and the calculations? Or if anyone would care to spell it out in super easy layman's terms, I would love it!

----

Indus,

Other than eigenvector centrality, I expect you to know how to perform all calculations... this is in part a class about measurements, so you have to know them. I can't exactly repeat the whole semester's lecture, so how would it be if you:

A) named some of the network characteristics you're most worried about,

B) tried a few examples here for yourself, and

C) checked with me to make sure you got it right?

I'd be absolutely glad to go through that with you. I'll be checking back here frequently.

Other than eigenvector centrality, which I'm not asking you to produce, the calculations are just some combination of counting, addition, subtraction, multiplication and division. The essence of these characteristics is simplicity. The hard part is remembering how to put all those parts together, and practice is the best way to accomplish that.

-- Best, Prof. Cook

# data[].attributes.body
Capricorn... I don't think we need to really know much about Eigenvector Centrality... from lecture 2 "In the Hansen text, don’t worry too much about telling the difference between the different kinds of centrality (degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality). We’ll get to those distinctions later on in the semester. Consider pp. 40-41 to be a teaser and move on." I could be wrong though.

--

Gliese,

I'm glad you aired your suspicion... so I could mostly agree in one sense but caution you in another!

I AGREE with you in regard to Eigenvector Centrality. I also said in Lecture 3, that I didn't want to to worry about "fi guring out exactly how eigenvector centrality is calculated (although the meaning of that kind of centrality is discussed below)."

I CAUTION you that you DO to know about the OTHER forms of centrality (betweenness, degree, closeness), because following up on Lecture 2, we considered those forms of centrality in Lecture 3.

Best, Prof. Cook

# data[].attributes.body
Can we just go back to the beginning and talk about Eigenvector centrality again. This is the only think i'm really not comfortable with. This is who knows the right people correct? what i have in my notes is the first step is to calculate the weighted degree scores, what does it mean by weighted?

----

Hi, Capricorn, and thanks for getting us started off! See above. I really only want you to understand the general meaning of eigenvector centrality and not how to calculate it, since that calculation is really complicated. The general meaning of eigenvector centrality is that it's higher for nodes that know lots of other nodes that, in turn, also know lots of other nodes.

-- Best, Prof. Cook