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




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