Blog 10: Aberrants

Detecting Criminal Organizations

What better way to identify criminal networks than to use their communications against them? This is what forensic detectives in law enforcement have used to trace who knows who in the network and potentially identify the crime lord. Since criminals use mobile phones in this day-in-age, it allows detectives to track their metadata and find out who they were in contact with. This presents the opportunity for SNA to shine in terms of using phone call record metadata to identify a potential structure of the criminal network itself.

Research Question:

Is there a way to detect a criminal organization network from phone call records?


By using LogAnalysis and communication data, criminal network structures can be unveiled to support law enforcement agencies with investigations.

Data collection:

The data in this case study were based off the results of a previous forensic investigation. The authors note for the sake of privacy protection, some information was obscured. In other words, potentially phone call metadata was used for the purpose of walking through this

Data analysis:

Network metrics like degree centrality, betweenness, closeness, and eigenvector centrality were analyzed in this study and modeled in LogAnalysis. This software is what forensic detectives use in the analysis of phone log records by means of a network representation. Nodes signify each unique cell phone (belonging to the criminals) whereas the edges represent the phone calls between the phones. The researchers went into extensive depth into their methodology of algorithm selection, clustering, and cutoff in the dendogram below. In the interest of keeping this blog post condensed, I won’t dive further into the intricate details of what else the authors did. But the takeaway is that their methodology has the potential of identifying (sub)groups of a crime network by using LogAnalysis and phone call records.

Ferrara, E., De Meo, P., Catanese, S., & Fiumara, G. (2014). Detecting criminal organizations in mobile phone networks. Expert Systems with Applications41(13), 5733-5750. doi:10.1016/j.eswa.2014.03.024

Preventing Adolescent Problem Behavior

Deviancy training is the concept where the communication of antisocial topics among peers lead to the encouragement and instruction for how to engage in these antisocial behaviors. With middle school being a pivotal period where friendships are chosen that promote problematic behavior, DeLay et al. decided to test if an intervention could impact this cycle.

Research Question:

Can a school-based intervention affect middle school friendship choices and, in turn, have a lasting impact on deviant talk with friends?


Assignment to the intervention would affect friendship choices.

Data collection:

Students from three middle schools were randomly assigned to participate in the study, where approximately 500 of 998 were placed in the intervention group while the rest were in the control group. The intervention group was placed in the SHAPe, a 6-week curriculum designed to engage parents and students to promote school success, healthy adolescent choices, and other methods that diminish problem behaviors. The control group was not introduced to SHAPe. At least one cohort of students were put through this study. Students were to self-report or be observed on the following: affiliation with deviant peers, friendship nominations, friendship interaction, deviancy training, and socioeconomic status. The students were followed up five years after the initial time of study.

Data analysis:

A longitudinal social network approach within RSiena (empirical network analysis package) was used to simultaneously account for and unravel selection from influence effects. The nodes were the students and the edges represent their friendships. Their findings suggest that public middle school intervention can change middle school, yet there is a bit of conflict in this finding. Only one of the three schools showed the intervention to influence friendship choices. There were also many limitations to this study, like how a great deal of turnover in friendships occurred over the five years… as we all have realistically gone through at this age.

DeLay, D., Ha, T., Van Ryzin, M., Winter, C., & Dishion, T. J. (2015). Changing Friend Selection in Middle School: A Social Network Analysis of a Randomized Intervention Study Designed to Prevent Adolescent Problem Behavior. Prevention Science17(3), 285-294. doi:10.1007/s11121-015-0605-4

Blog 9: KOD

The title of this blog post is KOD, which is the most recent album released by J. Cole. His album covers the dangerous reality of addictive behaviors as seen among his friends and family. I attached a 20 second snippet of his song FRIENDS in the album picture below. So what does this have to do with SNA? As you read through this post, you will understand how addiction can tie with family and friendship SNA.


The aim of this study was to apply social network analysis to pathological gambling in a comparison to recreational gamblers. For clarity, pathological gambling is an impulse control disorder defined by symptoms such as loss of control of gambling, preoccupation with gambling, and persistence despite negative consequences (1994). At the time of this study, a social network analysis was never done towards the subject matter of pathological gambling. Social network analysis methodology can provide insight as to whether individuals with pathological gambling seek out those with similar complications or whether social groups can directly deteriorate the risk for developing pathological gambling

Research Question:

Does an individual’s social network play a role in their pathological gambling behavior?

Is there a difference in network composition between pathological gamblers and non-pathological gamblers?


Pathological gamblers would have social networks that were denser with gamblers and be structurally different than non-pathological gamblers.

Data collection:

The study collected data using an egocentric network analysis approach from forty frequent-gambling adults recruited from the Athens, GA community. They were instructed to list their 30 closest social associates (ex. Friends, family members, romantic partners, co-workers) and demographics about each one of them (ex. Sex, race, years known, how close they were, etc.). What was truly measured was how frequently they gambled with or without the study participants. The assessment was done via EgoNet, a program used for collecting egocentric social network data.

Data analysis:

Each research participant had their own egocentric network, with nodes representing their social associates and edges being their connections (ex. Who knows who). Figure 1 shows example social networks of gambling in two participants of the forty studied. (a) Non-pathological gamblers network’s gambling and (b) Pathological gamblers network’s gambling. The darker colors and larger nodes reflect more frequent gambling behavior. They found that there was a significantly greater occurrence of gamblers in the networks of pathological gambler participants compared to those of a non-pathological gambler’s network.

Figure 1

Diagnostic and statistical manual of mental disorders. (1994). Washington, DC: American Psychiatric Association.

Meisel, M. K., Clifton, A. D., MacKillop, J., Miller, J. D., Campbell, W. K., & Goodie, A. S. (2012). Egocentric social network analysis of pathological gambling. Addiction, 108(3), 584-591. doi:10.1111/add.12014

Alcohol Use

I didn’t realize this until nearing completion of the blog, but this study is by the same authors of the Gambling study but done at a later year. The aim of this study was to apply social network analysis to addictive behaviors through the various levels of alcohol usage. In other words, authors wanted to see if at-risk drinkers had networks of peers with addictive behaviors. Social network analysis methodology can help identify clusters in the network so that interventions can take place to address their behaviors.

Research Question:

Is addictive behavior spread evenly throughout networks or more localized in clusters?

Is there a difference in network composition between individuals with various alcohol usage levels?


At-risk drinkers would report greater frequencies of addictive behaviors among their peers and be structurally different between alcohol usage levels.

Data collection:

The study collected data from 281 undergraduate students enrolled in a psychology course at a large university in the Southeastern United States. The students were given an ego questionnaire with the Alcohol Use Disorders Identification Test (to assess their alcohol use and related problems) and a social network questionnaire (like the one given in the Gambling study above). The addictive behaviors included in this study were alcohol use, marijuana use, tobacco use, and gambling.

Data analysis:

Pearson correlations were conducted to test whether each participant’s listed associates engage in addictive behavior. The study also utilized the Markov Clustering algorithm to maximize ties between participants and their associates within clusters while minimizing ties between clusters. Figure 2 displays a graphic representation of the different drinker types and their networks. Darker colors reflect higher prevalence of alcohol use by associates. And because the participant had ties between their listed associates, ties were only shown among associates who knew each other. The study found significant differences in drinking frequencies between the overall alcohol use networks among the participants. I would personally like to express my appreciation of this study visually categorizing each participant’s associates (as seen in the Key of Figure 2).

Figure 2

Meisel, M. K., Clifton, A. D., MacKillop, J., & Goodie, A. S. (2015). A social network analysis approach to alcohol use and co-occurring addictive behavior in young adults. Addictive Behaviors51, 72-79. doi:10.1016/j.addbeh.2015.07.009

Blog 8: HPAIV

The article I chose to discuss was Risk-based surveillance for avian influenza control along poultry market chains in South China: The value of social network analysis by Martin et al. (2011). As the title states, the authors were performing a surveillance for avian influenza viral spread (HPAIV) since poultry markets are so concentrated throughout certain areas in China. What makes this a public health issue is how pathogenic the avian virus is in these Chinese markets. Martin et al. utilized social network analysis to provide a network-based tactic to offer new perceptions on disease transmission dynamics.

The research question asked if an association existed between poultry trade network characteristics in southern China and HPAIV infection status by the occurrence of poultry outbreaks. They collected their data by sending surveys to thirty live bird markets in South China in Hunan and Yunnan provinces and Guanxi autonomous region. The survey asked questions in regard to hygiene and trade-related indicators of each market. In their network, the nodes were the source markets and the edges were the connections between the markets.

In this study, the K-core is a network metric that measures the centrality of a node within the network of live bird markets. Markets with a higher k-core value (ex. 4) play a greater role in the maintenance of HPAIV. The results yielded that China has had successful control in HPAIV outbreaks, as no outbreaks were reported in 2010. What I really like about this study is the way Martin et al. displayed their social network analysis data. They took the traditional method of creating an SNA graph using k-cores as categorical colors and placed them on the map. I had not thought about combining the talents of an SNA visualization software like Gephi with the likes of geographic Information system (GIS) software. The result turned out beautifully as seen in the figure below.

Blog 7: Decentralized

Habermas defines the public sphere as the nexus between public life and civil society. It is a neutral social space where private citizens can engage in debate with issues important in social life. It should also be separate from the highly political domains of state and economy, which allow for democratic freedoms such as expressing one’s personal opinions freely. In a democracy, public opinion is generated through the public sphere, which can support, challenge, or influence the operations of the state or governing body.

The main ideal behind the public sphere is that everyone should have equal access, autonomy, and opportunity to it. At first, this was not the case due to social norms such as inequalities of race and gender having a stake in the grounds of America. Fortunately, movements around the 1960s helped amend this negative social norm, thus making the public sphere true to its name and open for all. But its open doors allowed the once innocent ideal of media to grow roots.

Originally, media outlets such as newspapers were meant to communicate the debates in the public sphere with everyone. Fast-forwarding to today, media outlets have evolved into news channels (ex. Fox News) that have taken the form of highly political and biased channels. Instead of communicating the public sphere, they exert their influence on the public sphere, thus influencing public opinion and diminishing the true ideal of the public sphere.

Public Sphere influenced by social networks.

Castells identifies the network society as a decentralized system that manages information within social networks using micro-electronic based technologies (ex. Internet, smartphones). The rise of the network society reshaping the public sphere thanks to societies no longer being attached to the same geographic space. Individuals can now communicate and exchange information with each other instantaneously through social platforms like Facebook, Skype, and Instant Messaging. Castells rolls these technological advancements into his network theory as ‘timeless time and the space that flows’ based on new forms of time and space that are not geographically bound.

These new forms of social organization can impact education in many ways. For one, college-level courses are now available online that transcend the limitations of a person dreaming of earning a degree who is working full time and does not have transportation to the college they wish to attend. Personally, this SNA course is a great example because of the video chatting and instant messaging capabilities of asking our professors for help. It is also changing the political landscape of government officials who wish to have better campaign results by being more engaged with the public. Health care has also moved towards this, as more services such as self-care are now available for one to check their blood pressure or sugar levels and upload them online for their physician to see. This beats the alternative of a disadvantaged individual trying to take work off to go to the hospital for something that can be checked at home. As for the economy, the stocks of different businesses are constantly being updated for the public to keep track of their investments.

Do I believe network society changes will improve the lives of people? I’m kind of on the fence on this… Castells does an excellent job defining the network society with such optimism, but there are many adversative variables. A agree with his opposers: Zygmut Bauman, who says there are numerous social and political complications for it to work, and Frank Webster, who says that there is a lack of importance of how individuals can reshape these networks. And the fact that network societies are not available to low-income countries is a definite con to the ideals of Habermas and Castells. I believe the public sphere and the network society need to be worked on to adjust for real-world situations and media outlet influence infecting the public sphere.

Blog 6: Agenda

My research project revolves around social influence within the Marvel Cinematic Universe (MCU). My main question is if there is an established presence of women and minorities across the MCU. Some minorities include African-American, Hispanic, and Asian race/ethnicities. The main characters within this study will be analyzed together as a sociocentric network. To create this network, data was obtained from a Wikipedia list of a main character appearances in the MCU. An article by Acton highlight the simplicity behind non-directional web-based data collection and the online collaborative that is Wikipedia (2010). Since my data collection was based on pre-existing tabular data, it was quicker to obtain the data. However, there is the chance of web-based data not having all of the information the researcher needs, which was true for my case. I had to take extra steps to assess each character’s alignment, sex and a systematic approach to determine their race. This was proven very difficult when I knew an actor’s race, but they play an alien. One example is actress Zoe Saldana’s Gamora, who is pictured below.

To my surprise, there was another social network analysis was done on the MCU by Everington (2016). Her research was meant to show directors the loosely connected characters in order to strengthen their success and provide actors the recommendations for being casted as a hero or a villain. She collected her data by going through the IMDB to tie actors (nodes) and their respective movies (edges). The figure below is a network analysis of actors in the MCU with colors based on centrality and size based on n-degree. The clustered yellow nodes represent actors who were only in one move and do not have any ties to other films.

To build on Everington’s research, I am using main characters as nodes instead of her chosen actors. Her study is also limited to movies in Marvel movies from Phases 1 and 2. My research now includes those in Phase 3 films and features the that and the Production Studio as an additional edge attribute. To go along with my research question, I can assess if there was a clear difference in the demographics of interest between the MCU Phases and the Production Studios. Overall, my research will better understand what the presence of these demographics means for our social capital consumption.



Acton, R. M. (2010). Principled research on social behavior in online contexts: Methods and applications for sociologists (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database.

Everington, C. (2016). Marvel Cinema Universe Network Analysis. Retrieved from

Blog #5: Central

As a refresher, nodes are identified as individuals or groups within a network that share links or relationships with other nodes. The centrality of such node identifies the importance of a node to the overall functioning of the network it resides in (Johnson et al.). Other measures of centrality used to describe the functioning of a network include:

  • Degree – number of ties adjacent to a particular node
  • Betweenness – extent to which the flow of resources of a network are controlled
  • Closeness – distance between a node and other nodes

Together, they can be used to understand or identify important stakeholders of the network and the flow of communication. In turn, this understanding can help other networks improve themselves or successfully dissolve the target network’s foundation.

My small research paper is focused on the Marvel Cinematic Universe (MCU) network of main characters. The characters would be the nodes, and the edges would represent their shared movie appearances. For those unfamiliar, the image below shows the main movies under the MCU. You’re bound to have seen at least one of these gems in your lifetime!

So why am I crossing the MCU with the ever-growing world SNA? Because the MCU is becoming a staple in entertainment, being discussed as a shared norm within or among groups. What makes the MCU so unique is that all of these different characters continue to interact with each other, even after their solo movie release. Of those characters, there’s bound to be one or a few that hold the highest centrality with the rest. But how many of them are of female or non-White? I will consider the MCU to be a dynamic network based on its topology continuously changing over time. This could mean that certain nodes and edges are either added or removed.

Meligy et al. introduced a very important metric to account for such a dynamic network… time intervals! In their paper, they took “snapshots” of their network of interest to show how much of a change it undergoes over a specific period of time. The figure below comes straight from their study, representing (a) a regular aggregated graph like the ones we are used to seeing in this course, and (b) a time-varying graph. You can see how the data changes and how everything isn’t always connected.

From Meligy et al. (2014)

For something as big as the MCU, it would be cumbersome to do a time-varying graph for each movie release, plus it would diminish the overarching point of the presence of certain demographics I am interested in studying. If I want to incorporate time in this small paper, I would need to split the movies into groups based on a certain major event in that movie franchise. Luckily, the MCU producers have already come up with a Phase system that organizationally splits these movies. Below is a little snippet of what I am referring to, though Phase Three is not included as we officially approach the end of it.

Goswami and Kumar recently discussed some interesting bits in data collection of online social networks (OSNs). Even though this doesn’t pertain to my current small paper, it could be what some of you may be looking for now or in the future. I highly recommend it in either way, especially since this is one of the most recent articles read in this course. Lots of their OSNs analyses are relevant towards currently used social media platforms (ex. Facebook).

  • OSN data is large and dynamic… which pose a challenge to filter out noisy (unnecessary) data
  • Online spammers can have a great presence in OSNs, so be cautious of including certain data
  • Metrics like betweenness and degree centrality are static properties, thus they change slowly over time

I did find it interesting how Meligy et al. have a different view of a static network/properties, such that it is only captured in only one point in time. Even if they have the same definition as Goswami and Kumar, it would help that they didn’t make the concept of “time” exclusive to dynamic networks. Could this be due to Goswami and Kumar being more recent than Meligy et al.? Not 100% likely, as they were published within a couple years of each other…



Continue reading “Blog #5: Central”

4. Strike

The term social capital has had its fair share of differing definitions, dating back almost 40 years. One major analysis of social capital came from Pierre Bordieu (1980), being “the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance or recognition.” In the article published, he pointed out three things actors could achieve:

  • Direct access to economic resources
  • Increase their cultural capital through contacts with experts or individuals of refinement
  • Affiliate with institutions that confer valued credentials

Since this was published in French, it didn’t take until five years for the concept to reach the minds of Americans. Another was from James Coleman (1988), defining social capital as “a variety of entities with two elements in common: They all consist of some aspect of social structures, and the phacilitate certain action of actors within the structure.” Over time, definitions erected from people such as Nan Lin, Mark Granovetter, and Robert Putnam. But in Alex Portes (1998) stated that social capital stands for the ability of actors to secure benefits by virtue of membership in social networks or other social structures.

What does bowling have to do with this concept of social capital? Think of it literally, as Putnam did in 1995. There was an overall decrease in team bowling and associated lack of communication, thus we are “bowling alone”. But are we still bowling alone or at all anymore? Doing some literal research online, I wanted to see what the trend of bowling participation has been like. White Hutchinson did a study on this exact question, and you can see in the figure below how it decreased from 2007 to 2011.

I bring this up because social capital involves the culture of individuals. It’s not that we aren’t bowling alone, I’d rather say that our culture has just shifted on to different sports like basketball and other sports that require team communication. And there are even e-sports (aka electronic sports) nowadays. This actually makes a good segway into the next question.

Has the internet altered social capital? Lin (2001) suggests that the internet gives a revolutionary rise in social capital, allowing individuals in networks to access so much information than ever before. As stated earlier, we can “bowl” through e-sports, communicate via the hundreds of social media platforms, and gather social capital information through search engines. The internet does not alter social capital in any negative way, but that doesn’t mean social capital is always going to come up as a net positive. Portes (2002) dove deeper into this realization by illustrating its demand to exclude outsiders, such as how crime networks operate. Remember Week 4’s practicum?

There will always value in social capital for research at large. For community health/engagement, analyzing social capital can reveal the source of health disparities across different neighborhoods. What resources do the community members need access to? And is that resource providing the best quality for them?

A couple research questions related to social capital which SNA could help answer:

  • Do college students obtain more social capital from their direct adviser and professors or from their peers?
  • Is there an indicative difference of interaction quality and social capital between e-sports tournaments and bowling tournaments?

3. Acquainted

What is the small world theory? How did the ‘small world’ revolutionize how we approach the ‘big world’ including ‘big data’?
The small world theory, as defined by Travers and Milgram (1969), is a phenomenon based on what the chances are that two people from the United States know each other. In their study, individuals from Boston and Nebraska were asked to generate acquaintance chains to a target person in Massachusetts. They found that the mean number of intermediaries between the starters and the targets was 5.2, meaning that it took nearly six connections from a starting individual to the target. Thus, why being called a “small world” when there seems to be an interconnectedness in a large society. This concept revolutionized the way researchers approach the big world and big data.

Why are weak ties just as important to the small world than strong ties? Or are they more important?
Acquaintance are defined as people we know slightly but are not close friends. We all have at least a few in our lives, but their importance is greater in the realm of social network analysis. The relationship we have with acquaintances are known as our weak ties. Strong ties would, inversely, be our relationships with close friends. Granovetter (1973) explains the cohesive power behind weak ties in a social network. Even though many social network studies emphasize strong ties, they miss how different groups can become connected. He argues that weak ties are opportunities for individuals, such as integration into more communities. The figure below by Porter (2007) visually shows what this means, along with some descriptions between strong and weak ties within a group network.

I personally like the example given by Johnson, Honnald, and Stevens (2010) is how non-profit organizations can use social analysis. By identifying and utilizing their weak ties, they can enhance their data resources for funding and grant requests. This helps to facilitate interorganizational connections among non-profits and help find the right community partners they need in the future.

And what does Kevin Bacon have to do with any of this?
In my book, Kevin Bacon is the actor who played as the main antagonist, Sebastian Shaw, in X-Men: First Class. I also heard his name referenced by Star Lord, from Marvel’s Guardians of the Galaxy, as a hero in the “best movie of all time…” Footloose. Spider-man quickly denounced that in Avengers: Infinity War by replying that it never was, but that’s a story for another time. What’s important about Kevin Bacon in respect to social network analysis are his Six Degrees. A game was developed by a few college students to test if Kevin Bacon was the center of the entertainment universe, and it became apparent that he was! To test this for myself, I used The Oracle of Bacon, to see if there was a link between Kevin Bacon and the current Spider-man actor Tom Holland. To my surprise, they are connected through one actor (see full image below)! If it is this easy in the world of entertainment, imagine the full implications in the United States!

Continue reading “3. Acquainted”

2. Perspective

Social network analysis is a highly valued component in our continued understanding of modern society. Why? Because we are all connected through numerous networks that originate in-person or online. If we can understand what connects us in these networks, then we can better understand ourselves. Speaking of understanding… social network analysis perspective is an additional concept that helps researchers better focus on exchanges between actors in a social network. Researchers aren’t the only ones to use perspective thinking. Businesses use this to understand the current market and pinpoint who to target for the profit of their product. The image below compliments what I mean for business application.

Since perspectives can always shift or change, the structure of the network data can change as well. The example Sylvia Keim gave in her article helped me realize what this meant. Thinking about a life-course perspective, a shift in the structure can take the form of moving out of a parental home, starting a family, gaining/losing friends, etc. That life decision a person made resulted in a shift in his/her network structure. Today, modernization and individualization can also shift a network structure, whether that may be intended or unintentional.

Social network analysis is different from traditional social science data because there is a greater focus on patterns of resource exchange relationships and by emphasizing empirical observation of these relationships. In time, this can indicate characteristics of positions held in a network as well as the structure of the network.

To answer how relational data necessitates descriptive analysis and make predictive analysis challenging, we will first need to define what these are. Relational data is data in social networks that identify relationships between actors. They can originate from theoretical concepts and pertinent data. What makes relational data unique is their ability to be easily expressed as graphs. Think of the graphs we made from our practicum assignments! Descriptive analysis simply explains any findings of any data. For relational data, descriptive analysis can assess any similarities or differences between networks and find any trends within a network. Predictive analyses use current data to predict future outcomes, which would be challenging to go off of relational data. Why? Because we are still scratching the surface of modern social network analysis.

1. Connected

From my prior understanding, the word “social network” was limited to a platform that individuals use to communicate. I now realize that I only scratched the surface of what it truly means.  Not only is it an app that I use on my phone or computer, but also represents the actual ties we have with each other. The main point behind Christakis and Fowler’s book is that for one to understand how networks function, they must understand the connections between individuals. The authors support this by giving many scenario-based examples throughout their book. They range from emotions, sex, politics, money, technology, and so many more.

I began to really see the validity of what Christakis and Fowler meant when I reached This Hurts Me As Much As It Hurts You, which turned out to be my favorite chapter of the book. This chapter covered many public health issues that are prevalent through social networks. The spread of STDs is a big topic in the field and being able to track individuals with STDs is a cumbersome task for an epidemiologist or researcher. They must find out who the individual with STDs had sex with, then who that person had sex with, then ectara. Below you will see the same figure the authors were discussing regarding the Colorado Springs STD outbreak study. It really shows how widespread STDs can become. And this also applies to germs, obesity, bacterium, and other viruses that still affect us to this day.

Network of men and women who were part of an epidemic of sexually transmitted diseases occurring over a two-year period in Colorado Springs.

Now let’s tread back to the social networks present via the internet with Christakis and Fowler’s points in mind. In the chapter Hyperconnected, they dove right into the popular online gaming, World of Warcraft. I’ve never played it, but I know people who know people that are in love with it. With the new perspective the authors gave me, the connections between individuals (or their avatars) are the games themselves. Millions of players play games online, no matter if its on PlayStation (me), Xbox, PC, or mobile. And that number is nothing compared to the billions connected through platforms such as Facebook, Instagram, or YouTube. And going into the social web even more, they could be even more connected through the groups they are in. If there are a couple hundred people in the Mario fan club on Facebook, they are connected through that, then more connections will form and spread. Our networks are fluid and ever-changing, and that’s what makes these human social networks so fascinating to me. So to answer the question: yes, I do agree with the central claims both authors make in this book.

Before I end this post, I wanted to share a quick clip from Marvel’s Ant-Man (2015). Luis is a character known as being “the guy who knows a guy” for the main character, Scott Lang, to validate the legitimacy of their next heist. You’ll quickly be able to see how Luis’ connections lay out in the video, and I never related that to social networking until reading this book.