Blog 11: Scale free networks

When reading Shirky’s article, Power Laws, weblogs, & inequality, it definitely felt a lot like watching Shirky’s Ted talk on how social media can make history. While the article and ted talk did have similarities, they did also have some differences between them. In power laws, weblogs & inequality, Shirky talks about how not everyone is going to be heard, even when someone creates a blog, it is not going to be guaranteed that everyone in the world will see their individual blog. It all depends on whether people share the blog, recommend the blog, and what one person reads may be completely different than what someone else is going to read, we are all different and have different interests. This also brings me to the next point about how not everyone will participate in every conversation. Those who have strong social ties to one group is going to have more communication with each other than a group of acquaintances or even friends of friends. When referring to the scale free network, “networks grow through the addition of new nodes linking to nodes already present in the system.”(europeanmedical) This could refer to the public sphere or the use of social media, because different people “nodes” are able to talk to each other in a community.


This image shows the nodes and their links in different social media platforms.


The Ted Talk was interesting because he talked about how much our communication methods have changed over the years, we went from having a printing press to having the internet and cell phones to communicate with each other. It was interesting when he says that if a person is good at creating groups then they won’t be good at communication, or when someone is good at communication, they won’t be good at groups. The internet does allow groups and communication at the same time because we are able to reach a large group of people at the same time, an example of this would include Facebook. When it comes to democracy, Shirky talked about how the Obama campaign allowed people to express their feelings and let their voices be heard without shutting them down. This does not happen everywhere, because in China, they have what is called the “great firewall of China.” They filter what comes into the country. This does not happen in the United States because there is more of a freedom of speech, where people are able to openly share their thoughts and ideas. This corresponds to the public sphere, where is where people are able to openly talk to others. In a democracy, they are able to talk about who they support, who they don’t support, why they support one person opposed to another. It is important that we have this ability in the country because we need to be able to have a society where we can openly talk to each other.

Social media has come a long way over the years. Even if I think about when I was younger, social media has changed in such a large way. There used to be Myspace, now there is Facebook. Cell phones used to be much different, a lot of cell phones didn’t have the capability to allow us go on the internet. Now, smart phones allow us to go on the internet straight from our phones. We are able to reach a wide range of people by using Facebook on our phones. This has been said to be a bad thing because our personal connections with people have decreased, since more interactions with others are online. Others would say that this is a positive thing because we are still connecting with others. I would say that it is a positive thing, any connection we can form with someone else is still a relationship. Social media will continue to change over the years because we are living in a technologically advanced world now.

This image shows a scale free network, which shows how different nodes have links attached to them from other nodes in the network.

Blog 10: Crime and deviance

The Community Concept in Criminology: Toward a Social Network Approach

Leighton, B. (1988). The Community Concept in Criminology: Toward a Social Network Approach. Journal of Research in Crime and Delinquency, 25(4), 351-374.

In this study, The Community Concept in Criminology: Toward a Social Network Approach, Leighton looked at what the concept of a community is and how it is relevant with regards to crime and deviance. The word “community” has a different meaning to different people. He brings up a good point about how the idea of a community has declined over the years. “In urban areas, social ties characterized as being weak, communal ties as being scarce, and community as having decayed if not declined into oblivion.” (Leighton, 1988) This paper was written in 1988, which shows how the idea of community was starting to decline back then, it is still the same way today in 2018. The rise of technology advances has contributed to that.

The nodes in this study are the individuals in the community, the edges are the links between the node and their social ties. Leighton defined a network as, “a specific set of linkages among a defined set of persons with the additional property that the characteristics of these linkages as a whole may be used to interpret the social behavior of the persons involved.” (Leighton, 1988) Meaning he is looking at the links a person has to determine how their behavior is affected by those individuals in their social network.

He says that “network density is perhaps the most important structural attribute: it is the proportion of ties known to each other independent of the individual under focus.” (Leighton, 1988) The density of a network refers to the connections that are involved. Density is definitely one of the most important structural attributes in a social network, because it allows the person looking at the structure of the social network to determine which individuals are most important to the network. The individuals who are closer to the center of the network are going to be more important to the individuals who are at the outer periphery of the network.

The nodes who have a link to another node who has shown that they have deviant behavior is going to also have a higher chance that they also have an increase in deviant behavior. Most people have strong ties to individuals that they are closer with, have more in common with, or people that they trust more. “Ties linking them to other deviants relative to the proportion of non-deviants in their network would be predicted to be likely to become deviant.” (Leighton, 1988) This would indicate that communities can be looked at when determining deviant behavior and crime. If someone has a strong social network in a community and they have been responsible for deviant behavior, the individuals they have a strong tie with are also at risk to participate with deviant behavior also.


While this picture does not relate to either article I found about their studies, it does show a social network with regards to crime. It shows central nodes in the network, and their edges that connect them. I found this to be a good visual to show a close up of a social network.

Neighborhood co-offending networks, structural embeddedness, and violent crime in Chicago

Bastomski, Brazil, & Papachristos. (2017). Neighborhood co-offending networks, structural embeddedness, and violent crime in Chicago. Social Networks, 51, 23-39.

In this study, Neighborhood co-offending networks, structural embeddedness, and violent crime in Chicago, they were looking at whether neighborhoods and communities contributed to the crime rate in the city. They wanted to determine if individuals who live in the same neighborhoods have a similar likelihood of crime. “We constructed a co-offending network using arrest data from the Chicago Police Department, where nodes represent unique individuals arrested by the police during this time period and each edge connecting the nodes represents an instance of co-offending.” (Bastomski, Brazil & Papachristos, 2017) In this study, they got their network by going to a Police Department and looking at individuals who had been arrested in Chicago, these individuals were each the nodes. They then looked at the people they may have had a connection to in their network, these individuals were the edges because they had a link to them.

Crime rates are more likely to be higher in neighborhoods where individuals may not have a high degree of people that they know (or edges) but it is more likely to happen in a neighborhood where they have a smaller amount of edges, but they are closer friends. “A neighborhood with a high degree and low embeddedness reflects ties that are potentially more vulnerable to disruption; whereas a neighborhood with a low degree and high embeddedness reflects a small cluster of highly inter-connected neighborhoods.” (Bastomski, Brazil & Papachristos, 2017) This means that neighborhoods who have individuals that are closer together, this could be a small town, that they are more likely to be a closer-knit community. This does not necessarily mean that crime rates are going to be lower, it just means that one node is going to have fewer edges, which could indicate a stronger friendship to the edges that they do have.

If a person is hanging out with a person who has been to jail before for a crime, and they are with someone who is a bad influence, they are more likely to be influenced by deviant behavior. “Employing co-offending network data presents several strategic advantages. First, criminological work has established that co-offending acts account for a substantial proportion of all crime.” (Bastomski, Brazil & Papachristos, 2017) Most individuals are going to carry out a crime with someone that they have a strong tie to, because that person is most likely someone that they can trust. The study talked about how a social network does contribute to the spread of violence in a neighborhood. A way that law enforcement can try to reduce crime is to try and come up with “an empirical approach for identifying neighborhoods that require the most assistance and intervention.” (Bastomski, Brazil & Papachristos, 2017) The focus would be on those neighborhoods that have a high level of crime and a high arrest rate of those individuals. Focusing on those individuals and finding out exactly what led them to deviant behavior is going to be one of the best ways to address crime rates. It is also going to help with intervention and finding out what more can be done in a neighborhood where individuals have a close social network.

Being able to use social network analysis is huge when it comes to looking at crime and deviance. When using social network analysis, we are able to look at what people are most important in a network, along with their closest friends or family, the people they interact with on a regular basis. We are also able to determine if a person’s close ties do have anything to do with the chance they will have a higher chance of deviance. Using social network analysis will continue to be important when looking at different social networks.


Blog 8: K-Core Brain connectivity

In this study, Breakdown of Brain Connectivity Between Normal Aging and Alzheimer’s Disease: A Structural k-Core Network Analysis, they looked at brain connectivity in a normal brain that was aging and a brain that has Alzheimer’s disease. In this study, they referred to the K-core as the structural backbone of the network. They thought that the K-core was an important way to look at how the K-core value would help with the understanding on how the connections in the brain work. “The k-core decomposition outputs a network core that consists of highly and mutually interconnected nodes.” (Daianu et al, 2013)



This image is from the study that shows the actual K core connections in the brain. (Daianu et al, 2013)




They said that when a K-core has a low value, that the K-core would not be highly connected because that would indicate a low degree number. When the K-core value had a higher number, then that indicated that they were more central in the network. For this study, they selected a K value of 18, which means that the nodes that have a degree of 18 or more are the ones that will be kept, and the ones that have a degree less than 18 will be removed.

The nodes in this study are the different regions of the brain, and the edges are the connections in the brain, which are shown by the different fibers. They made a matrix which showed the connections, they had a total of 111 subjects they looked at. After looking at the K-cores in the network, they were able to look at disease in the brain. “First, the k-core loses nodes drastically as disease progresses, so the number of nodes present where asymmetry can be detected is falling rapidly.” (Daianu et al, 2013) Since the nodes in this study are the regions of the brain, this shows that the regions of the brain are affected by Alzheimer’s disease compared to the brain image they looked at of individuals with a normal aging brain. “The k-core did indeed enhance the disease effects, as the entire k-core was ‘‘lost’’ in the left hemisphere of AD subjects. These findings are important to locate brain regions that change with disease progression.” (Daianu et al, 2013)


This image is a good representation of how the diseased neurons do not look as connected to the brain as the healthy neurons do.

This is to be expected, as with Alzheimer’s, “abnormal deposits of proteins form amyloid plaques and tau tangles throughout the brain, and once-healthy neurons stop functioning, lose connections with other neurons, and die.” ( With this study, the K-core provided valuable information that showed them the areas of the brain where connections were lost. This is important in finding out which regions of the brain are most affected by this disease.

Alzheimer’s Disease Fact Sheet. (n.d.). Retrieved from

Daianu, M., Jahanshad, N., Nir, T. M., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2013). Breakdown of Brain Connectivity Between Normal Aging and Alzheimer’s Disease: A Structural k-Core Network Analysis. Brain Connectivity, 3(4), 407-422. doi:10.1089/brain.2012.0137




Blog 7: Habermas and Castells

The Public Sphere is declining, “If we are to believe what sociologists are telling us, the public sphere is in a near terminal state.” (Johnson, 2006) Habermas defined the Public Sphere as both the public and the state working together and communicating with each other. A democratic society is a good example of a public sphere because the public are able to have a say in what they what. People with similar and different ideas are able to come together and talk about what they want. The Public Sphere is “a willingness to engage with the particular issues thrown up by contemporary politics is, for Habermas, a central responsibility of the critical theorist.” (Johnson, 2006) Meaning it is good to engage with each other about different issues.

“An” Understanding of Habermas and the Public Sphere

Castells defined a network society a little differently, he defines it as more of an online way of communicating instead of in person. “Castells defines ‘network’ explicitly as a set of interconnected nodes of which he mentions such examples as stock exchange markets and their ancillary centers of advanced financial services in the network of global financial flows.” (Anttiroiko, 2015) Nodes are important and are connected to each other in different ways.



This is a good image that shows how public opinion can be influenced by media.

Having a network society can be both a good thing and a bad thing. While online communication can be good, it also takes away from in person relationships. It is also a problem when some people may not have internet, so it hasn’t really improved their lives any. When it comes to education, we have seen over the years that learning has focused more on technology. Today, kids take laptops with them to class to take notes on, whereas before that wasn’t an option. Also, in health care, a patient’s health file used to be stored with lots of other files, but we are seeing more and more that even with health care, those files are now online. Technology is always changing and everywhere we look, there are always new advances when it comes to technology.

Anttiroiko, A. (2015, July 15). Networks in Manuel Castells’ theory of the network society. Retrieved from

Johnson, P. (2006). Habermas: Rescuing the Public Sphere. London: Routledge. Retrieved from,url,cookie,uid&db=nlebk&AN=157805&site=ehost-live&scope=site

Blog 5 node centrality

When it comes to social networks, there are four main node centrality measures that describe networks. Node centrality measures are important in social network analysis, but also in other fields when doing research. Degrees look at how many links a node has to other nodes in a network. Betweenness centrality looks at the shortest path between nodes. Closeness centrality refers to how close a node is to other nodes in a network, and eigenvector centrality looks at how important a node is, it looks at how many other links to other nodes in a network there are. If a node has a high eigenvector centrality, it is well connected to other nodes, it is important.


This is a good example of how people are the nodes, it shows the links between all of the nodes in this particular network. Some are closer together than others.

Node centrality measures are not only used when looking at social networks, they can be used in the health field also when looking at infectious diseases and how they spread. “In epidemiology, some possibly infective contacts between individuals are long term (friends, family) but many are fleeting (people in the street or the market place).”(Hyoungshick & Anderson, 2012) When it comes to infectious diseases, it is important to find the node that had the most contact with others, they may be long term and people they have been in close contact with over a period of time, or they may be short term, If someone is sick on an airplane, and they touch their tray, then it doesn’t get cleaned completely, the next person who sits there could get sick. They didn’t have a long-term connection to each other, in fact, they didn’t really have a connection at all, apart from the airplane tray. In the article by (Hyoungshick & Anderson, 2012), they looked at merchants and villages and the closeness and betweenness values between them to look at how diseases spread.

This picture shows an initially infected adult and the different links it has to other nodes(people). It is a good way to show how diseases can spread.

In cases of Ebola or STD’S it is important that they find who came into contact with the person who is sick. The same goes for vaccinations and how they protect against disease. “If a node has twice the neighbors of another, it has twice as many nodes to which to spread an infection.” (Holme, 2017) Meaning they not only have twice the way to spread an infection, but also get an infection. If someone has a close degree centrality and they are sick, it means that they are looking at all of the links one person has to another (who they have come in contact with) When looking at closeness centrality, this looks at how close a person is to other people. Betweenness centrality would look at the shortest path between two people, and eigenvector centrality looks at how important one person is in the network and whether they had come into contact with a lot of people. If they had, this means they are a significant person, they might be a carrier of the disease. This shows that node centrality is important, not just with social networks, but even when health professionals are looking at the spread of disease.

Holme, P. (2017). Three faces of node importance in network epidemiology: Exact results for small graphs. Physical Review E, 96(6). doi:10.1103/physreve.96.062305

Hyoungshick, K., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E,85(2). doi:10.1103/physreve.85.026107

Bowling alone and Social Capital: Blog 4

Growing technology advances have increased over the years, which led to the idea from Robert Putnam that we are all bowling alone. Bowling alone refers to the idea that communal participation in the U.S. has been declining and people do not bowl in teams anymore, they bowl alone. (Kadushin, 2012) While there may be a decline in face to face interactions, the use of technology still provides us the ability to have strong ties and relationships with others. While bowling in groups may have decreased over the years, bowling alone has increased. That isn’t to say that we are all isolated, but with social media and cell phones, there became less of a need to go out and talk face to face. Even though in person interactions decreased, technology still has the ability to bring people closer together.

This picture shows just how many Facebook users there are. This was from January 2018, so I am sure the number has only increased since then. It just shows how social media is a big part of our lives now.

Now, people are able to stream movies and shows on Netflix or Hulu, even cable has changed over the years. Social media went from Myspace to Facebook, even my Grandma has a Facebook now. Even if I am less socially engaged in person, I am more connected through technology. I was born in Scotland and moved here when I was younger, so I still have a lot of family there. Social networking has given me the ability to talk to my family in Scotland who I wouldn’t be able to talk to regularly otherwise. I am also able to see them on Skype, and even though we aren’t directly in front of each other, I am still thankful for the technology that allows me to see them on a computer. Even since I was young, there have been many changes in terms of computers, internet, and cell phones. It makes me wonder what other advances are still to come.

Social capital Is “everything psychological and social about a person”. (Kadushin, 2012) Our social capital refers to our connections in our social networks, whether they are strong or weak ties. Our social capital is important because it gives us the ability to connect and form relationships, it brings people together. Trust between people is important when it comes to social capital, if we have something personal going on, we usually reach out and talk to someone from our strong ties, whether that be a family member, a close friend, or someone we are in a relationship with. We can trust them.

Reciprocity is part of a social capital, but I think this is an example of how social capital may not always be positive and how people could use social capital to their advantage, but not necessarily in a good way. If I volunteer for something, help a friend, or do something for someone else, I don’t do it because I expect something in return, which is what reciprocity is. It is the understanding that if someone does something for me, I’ll do something for them in return. I don’t think that is how relationships and connections between people should work. I would like to think that most people don’t think this way, and that they do things for others just because it is the right thing to do and not because they will use that for something for themselves in the future.

This picture shows what goes into social capital. The networks in social capital consist of brides and bonds.

Social capital can definitely be useful when it comes to research because we can reach out to our strong ties to conduct our research. We could also reach out to our weak ties if we needed more participants and more data. When collecting data for my own research, I can reach out to strong ties first because they are the people I am closest to, then I can start expanding the people I reach out to, I would start asking weak ties (acquaintances) that I don’t talk to regularly but I may be friends with on Facebook. Social capital helps in our daily lives because it gives us the ability to form relationships and connections, no matter how big or small with other people.

Kadushin, C. (2012). Understanding social networks: theories concepts and findings. Oxford: Oxford University Press.

Blog 2: Social networks

Social network analysis is important in modern society because social networks are used every day. An example would be Facebook or Twitter. “Facebook where the links indicate friends or links, or Twitter where the links may be retweets or followers.” (Yang & Keller, 2017) Social network analysis is important because we form relationships and have relationships, whether that be with family or friends or meeting new people and starting new relationships with people. Social network analysis looks at relationships between actors, which are also known as the nodes, and the ties, which are the edges, between them. “Social network research can be seen as one approach to dealing with a central problem in social theory, which is to capture the relationship between the individual and society.” (Keim, 2011)

This shows how many social networks there are out there. I thought this picture did a good job of showing the different types of social networks, whether it be social media, search engines, communication apps, etc.

When it comes to social networks, there can be positive and negative relationships or strong and weak ties. I didn’t realize that social network analysis also included negative relationships. When I hear the word social networks, I always thought of positive relationships. My thought process was one sided because I mainly thought of social network analysis having to do with the internet, not people.

 This shows how strong ties and weak ties work. It shows the individual and how they have a few close people to them which are the strong ties, then the people on the outside are the weaker ties.

Social science looks more at an actual sample of what is being studied, it deals more with math, such as statistics or graphs. Traditional social science research is linear. “Social science involves social entities involved in social action.” (Robins, 2015) Relational data looks at different connections and ties to people in social networks, what connects one actor to another. When I think of relational, I think an example would be coworkers. The connection between them is that they work together. Descriptive analysis goes more in depth about the research, it can look at what makes networks the same or whether they have differences. It would look at the types of actors involved and how relationships are formed. Predictive analysis takes what has already been learned and takes data that has been collected and it looks at predictions about what might happen. This doesn’t always work for all research, it depends on what is being looked at. While a lot has been learned about social network analysis, there is still a lot to learn.

Keim, S. (2011). ‘The Social Network Perspective.” in Social Networks and Family Formation Processes.

Robins, G. (2015). Doing social network research: Network-based research design for social scientists. Los Angeles: Sage Publications.

Yang, S., Zhang, L., & Keller, F. B. (2017). Social network analysis: Methods and examples. Los Angeles: Sage.

Linked Barabasi

Barabasi talks about how networks are found everywhere. I didn’t realize that networks could be found in math and science, that is talked about in this book. I had an understanding that networks were mainly related to computers, but what this book is trying to say, is that networks are more than that. The use of nodes and links are used when it comes to talking about networks. An example would be a computer, which has wires, the wires are the links. When we go on the internet, that is an example of a connection. The internet is a way to get information out easily and spread to many people at once.

This picture shows nodes and links in a social network.

Barabasi looked at Euler’s work on graph theory, which allowed nodes to be shown on a bell curve or normal curve, just like we would see with graphs in math. He also found that hubs are where nodes are connected.


This picture is a great representation of social networks. It shows the nodes and links.

Barabasi also mentioned the six degrees of separation. Meaning, chances are if we know six people that we will most likely know another six people that they know. This means that the people are the nodes and when they interact with each other, that connection between them becomes a link. I am not sure I completely agree with this. Since there are so many networks that one person can be a member of, it is unlikely that they will know people from outside social networks that they don’t regularly interact with. For example, if I know six people from work, chances are, I won’t know six other people that they know outside of work. While networks are everywhere, I don’t think networks are that small.

I did find it interesting how networks are found practically everywhere. Where I do agree with Barabasi, is when he talks about how technology will continue to improve. Even from the time this book was written to now, there have been many advances that have been seen over the years, this will continue to happen.

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