Blog 10: Hackers and Smokers

The first article used social network analysis to understand the network structure of the hacker group “Shadowcrew.” Due to rapid advancements in information technology, the authors were attempting to assess how hackers were interacting and communicating within this deviant group to develop their existing network structure.

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Research question:  The study had 4 research questions:
1. What is the network centralization of a hacker network?
2. Are there members of a hacker network who stand out as critical leaders?

3. How strongly do leaders influence a hacker network?
4. What subgroups exist and interact in a hacker network?

How is the data collected: A search was conducted using LexisNexis Academia, using the exact keyword Shadowcrew.  LexisNexis Academia includes information from major newspapers, journals, and law/criminal proceedings.  The specific sources of data are listed below and the varying sources of information adds to the strength of the study.  The data was then mined using AutoMap.

What is the sample population:  Twenty three members of Shadowcrew, as identified using the method above, are the sample population.

What are the nodes:    Each node represents an individual hacker (figure 2).

What are the links:  The links represent communication or collaboration between hackers (figure 2).

Results:   There were pronounced differences in measures of centrality among hackers (table 4).

As it is evident in table 4, Andrew Mantovani consistently had  high values on measures of centrality.  Thus, he was identified as a leader.   To add further support, historically he is the co-founder and network administrator for Shadowcrew.  Below is a picture of Andrew.
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Andrew had significant influence in Shadowcrew as evident in figure 3.  The people that he is connected to were key players in their group as evident by centrality measures.

Distinct subgroups existed in shadow crew as shown below.

An understanding of SNA allowed for the identification of the leader of Shadowcrew as well as his top lieutenants.   It conveys the power of measures of centrality in identifying salient nodes in a network.    SNA also allowed for the identification of cliques. These findings derived from SNA are important because it allows for law enforcement efforts to be more efficient, targeted towards key players.

 

The second article investigated smoking among adolescents. The authors believed that the position of an adolescent in their peer group would influence smoking.

Research question:  Do adolescents in various social peer group positions have different rates of  prevalence for current smoking?

How is the data collected: A survey was used to obtain smoking status, relationship and peer group information from ninth grade student in the North Carolina School System.

What is the sample population:  One thousand ninety two adolescents ninth grates that attended public school in North Carolina in 1990.

What are the nodes: Each adolescent surveyed is a node in the network.

What are the links:  The links represent a best friend relationship.  Students were asked to named their top three best friends, in decreasing order of priority.

What are the results:  Rates of smoking varied significantly across schools.  As table 2 demonstrates, social position did affect status as current smoker in 4 of the 5 schools.  Isolated adolescents were more likely to be smoker.  The results are mixed for clique members and  liaisons.  Liaisons interact with a clique but they are not members.

By understanding the social network, it became possible to determine which adolescents were isolated, liaisons and clique members.  Using this data the researchers were able to understand current smoking patterns in a social context.

References:

Ennett, S. T., & Bauman, K. E. (1993). Peer Group Structure and Adolescent Cigarette Smoking: A Social Network Analysis. Journal of Health and Social Behavior. https://doi.org/10.2307/2137204

Lu, Yong; Luo, Xin; Polgar, Michael; Cao, Y. (2010). SOCIAL NETWORK ANALYSIS OF A CRIMINAL HACKER COMMUNITY. The Journal of Computer Information Systems. https://doi.org/10.1108/13685201211238016

Blog 9: Social Network Analysis at the Community and Friend Levels

The first study investigated collaborations networks in the social work academic community using social network analysis. Their research question: have collaboration networks for scholars in the social work community changed overtime?  Their sole source of data was the Social Work Research Database which is a comprehensive collection of published social work articles.  The sample population includes scholars of social work that have published in the field (N=19,789) and these scholars compose the nodes of the network.  The links of the network represent collaboration where authors shared authorship on a research article.

Their primary conclusion is that levels of collaborations have increased significantly from 1990 to 2014, and findings were consistent with the Matthew effect.  The Matthew effect can be conceptualized using the adage, “the rich get richer and the poor get poorer.” It has three properties: proportional growth, cumulative advantage of a few nodes, and preferential attachment to central nodes.

As figure 2 demonstrates, there was proportional growth from 1990 to 2014.  The increase in network size provides additional nodes for potentially more edges to be formed.   Figure 2 also supports preferential attachment to central nodes because dark spots more central in the network increased in size and frequency.

Table 3 clearly demonstrates cumulative advantage.  Those authors with higher degrees to start increased from 17.2 to 126.7 degrees, while authors with smaller initial degrees remained small.

Satisfaction of the Matthew effect means that most of the new collaboration in figure 1 in the scholarly social work community occurred for those already advantaged in the social work community.

Quantitative and qualitative methods of social network analysis helped operationalize the data collected in a usable form.  Quantitative measures of degree clearly demonstrated levels of collaboration for each author.  Qualitative analysis of proportional growth (figure 2) allowed us to see that there was preferential attachment to central nodes.

The second articles investigated processes of assimilation and contagion in social networks of adolescent friends.  Their research question: does assimilation and contagion affect symptoms of depression among friend?  The study obtained data from a clinical trial on substance misuse which was conducted for 19 schools in England.  The sample population includes 9-year-old students from 7 of the 19 schools (N=1,114).  The nodes represent students and the links are contagion or assimilation processes among friends.  Consistent with contagion, those adolescents with friends having greater symptoms of depression also found their personal depression symptoms increase.  Assimilation was also supported.  Adolescents were likely to see their depression symptoms change to a level similar to their friends.  The use of contagion and assimilation as tools for social network analysis allowed us to determine the transmission of depression symptoms among adolescent friends.

Bibliography

Markus Eckl, Christian Ghanem, Heiko Löwenstein; The Evolution of Social Work from Disconnected Groups to a Scientific Community: A Social Network Analysis, The British Journal of Social Work, , bcy050, https://doi-org.proxy.library.vcu.edu/10.1093/bjsw/bcy050

Doucet, C., Lacourse, E., Vitaro, F., Stewart, S. H., & Conrod, P. (2016). 6.101 DO FRIENDS MATTER FOR ADOLESCENT DEPRESSION? A LONGITUDINAL SOCIAL NETWORK ANALYSIS. Journal of the American Academy of Child & Adolescent Psychiatry. https://doi.org/10.1016/j.jaac.2016.09.420

 

Blog 8: K-Core Decomposition and Density Mapping of the Cerebral Cortex.

Drawing on my neuroscience background, I chose a study that operationalized k-core decomposition and density to understand the structure of the human cerebral cortex.   Their research question: how can the cerebral cortex be mapped quantitatively to help us understand process and function.  Prior research has looked at connectivity within the cerebral cortex, but they were unable to map out the structural core, network modules and hubs objectively.  By applying k-core decomposition and density analysis neural pathways more at the periphery could be removed in a controlled fashion to provide a distillate of the structures involved in information processing.

The data was collected using diffusion spectrum imaging which allows for a detailed representation of pathways within the human brain.  As it is evident in the picture below, it allows for the obtainment of quantifiable data.

The sample size consisted of 5 participants whose diffusion spectrum images were averaged to increase generalizability.

The nodes represent neural structures involved in information processing and integration.

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The links are axonal pathways that interconnect the brain.

The results indicate the presence of large structural networks within the brain.  Structural cores were revealed in the posterior medial and parietal cortex.  Network modules were present in the temporal and frontal cortex.   Structural cores had high degree and betweenness centrality values and served as connector hubs that linked significant neural structural modules.

While the images and the study may appear simple.  This represents research that could hardly be imagined during my undergraduate studies at Berkeley.  Back then, people were injecting dyes into each axon and following its path to understand interconnections (image below).  Now they are not only able to visualize these connections but obtain quantifiable data directly from the human brains.

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This level of understanding of the human brain is startling and scary.  If the processing power of the brain is fully leveraged in technology,  it can have many unforeseen consequences that cannot be controlled.

It reminds me of what Facebook ran into when developing artificial intelligence (AI).  What they found was that their AI created its own programming language that could not be deciphered by leading computer scientists.  Once this happened the AI was shut down but under less controlled settings it could have resulted in disaster.

References

Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Van Wedeen, J., & Sporns, O. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology. https://doi.org/10.1371/journal.pbio.0060159

Omatu, S., Malluhi, Q. M., González, S. R., Bocewicz, G., Bucciarelli, E., Giulioni, G., & Iqbal, F. (2015). Distributed computing and artificial intelligence, 12th international conference. In Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-319-19638-1

Blog 7: The Public Sphere, Old and New.

Public sphere as a construct is introduced by Habermas.  The public sphere represents a neutral common where people can share opinions and thoughts engaging in democracy outside of governmental or organizational influence (Habermas, 1989).  Its formation can be traced back to 18th century Europe when feudalism and church power were both dissipating creating a vacuum.  Initially dominated by the bourgeoise, coffee houses and saloons served as public spheres and newspaper helped to further disseminate information.  However, a transition occurred with participants in the public sphere shifting from the bourgeoise to people in general.  It became more open and all that was required for participation was mutual interest to discuss matters of public importance.  Public spheres can be successful in the absence of hierarchy dependent on the extent of access and quality of discussion.

Habermas feared that with the formation of large organizations and governments that characterized the Industrial Revolution, the influence of the public sphere would be decreased due to the “refeudalization of power.”  Power would again be concentrated among the few (Khan, 2014).  Organizations would attempt to maintain the facade of open discussion to influence people, but people would no longer have real say.  Even the media can be viewed as consistent with this concept because they represent organizations rather than public interests.

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Network society as used by Castells refers to social structures characteristic of the Information Age.  No longer are bureaucratic and hierarchical institutions the dominant force.  In their place, decentralized networks are prevalent and growing, fueled by microelectronic communication technologies (Castells, 2003).  This decentralization of the generation and spread of information within society has become the new public sphere.  Network societies are viewed by Castells to facilitate communication outside of hegemony.  It opens the minds of the people involved because it presents a new forum where education can take place.  In turn, this education can lead to social movements.

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This new public sphere draws on the power of communication (Fuchs, 2010).  Fear is the oppressor of mobilization and to counteract its effects, information must reign free.  The network society as characterized by decentralization, means that this knowledge and information comes from the people (Sampedro & Martinez, 2007).  The spread of such information puts power back into the hands of ordinary citizens.  It enables them to have a voice that others can galvanize around helping them to exert influence over governments, economies and healthcare systems.

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The hyper-connectivity characteristic of network societies can be harmful as well as beneficial.  We must be vigilant.  As we discussed earlier in our course, this type of connectivity allowed the porn industry and white supremacists to reach a broader audience.  However, of greater significance, it allowed for greater coordination across societies because discussions could take place helping to reshape the structure of society according to what people value.  By placing power back in the hands of ordinary people, over time, it has the potential to significantly improve lives.  Communication is the essence of social organization and we must keep it free of special interests.

Can you think of ways that this new public sphere may again be encroached upon?  Has this already happened to some degree or are people mostly able to communicate in a public sphere?

Bibliography:

Castells, M. (2003). The internet galaxy: Reflections on the internet, business and society. Research Policy. https://doi.org/10.1016/S0048-7333(02)00012-4

Fuchs, C. (2010). Communication Power. Information, Communication & Society. https://doi.org/10.1080/13691180903390885

Habermas, J. (1989). The structural transformation of the public sphere: an inquiry into a category of bourgeois society. Contemporary sociology. https://doi.org/10.2307/2072652

Khan, M. Z. (2014). Revitalization of the Public Sphere: A Comparison between Habermasian and the New Public Sphere. Acta Universitatis Danubius. Communicatio.

Sampedro, V., & Martinez, M. (2007). The Digital Public Sphere: An Alternative and Counterhegemonic Space? The Case of Spain. International Journal of Communication.

Blog 6: AHA PAC in response to HVBP

My research project will attempt to answer the question of how the American Hospital Association Political Action Committee (AHA PAC) has responded, in terms of political financial contributions, to the introduction and implementation of the Hospital Value-Base Purchasing (HVBP) Program by the Centers for Medicare and Medicaid Services (CMS).  More specifically, three time periods will be studied.  The first period includes three years prior to the announcement of HVBP by CMS to Congress, from October 2004 to October 2007, to establish a baseline of pre-HVBP spending.  The second period includes the announcement on November 2007 and follows spending patterns up till July 2011, when financial incentives took effect.  The last period, from August 2011 to August 2014, will follow AHA PAC spending patterns after the full implementation of HVBP.

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My data will be obtained from the Federal Election Commission (FEC).   This source was selected because of the extensive amount of validated information available.  Professor Pastore will be instrumental in providing cleaned useable data that is ready for analysis.  The network(s) nodes will consist of political organizations or other entities, and the edges will include financial contributions that are both sent and received.

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A systematic review of databases PubMed, EMBASE, and ABI/INFORM did not produce any peer reviewed articles specific to the “Federal Election Commission” and “American Hospital Association.” However, a similar analysis was conducted regarding the American Medical Political Action Committee (AMPAC), a division of the American Medical Association (AMA), in terms of campaign contributions from 1989-1990 and 1991-1992.  They specifically looked at the spending patterns of the AMPAC in relation to the “gag rule,” which limits discussion of abortion in federally funded clinics (Sharfstein & Sharfstein, 1994).  The approach of their analysis is parallel to what we propose in our current study.  Political contributions will be tracked for a specific organization to determine sending and receiving patterns as it relates to a political issue.

Despite the very large amount of literature that HVBP has generated, until our research, no one has studied how the AHA has responded to the political climate.  Our research will build on the very limited amount of work that exists between healthcare related legislature and healthcare political action committee spending.  The hope is that our research will increase transparency and insight, providing us with a more comprehensive understanding of politics in healthcare.

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From our readings, our approach to data collection is very similar to that proposed by Fu (Fu, 2005).  In the article, daily contacts were followed over time to develop sophisticated contact diaries.  In essence, the FEC data is providing us with this daily contact information and we can analyze the data according to the relevant time periods.

References:
Fu, Y. C. (2005). Measuring personal networks with daily contacts: A single-item survey question and the contact diary. Social Networks. https://doi.org/10.1016/j.socnet.2005.01.008

Sharfstein, J. M., & Sharfstein, S. S. (1994). Campaign contributions from the American Medical Political Action Committee to Members of Congress. For or against the public health? The New England Journal Of Medicine. https://doi.org/10.1056/NEJM199401063300107

Blog 5: Data Collection, Analysis, and Node Centrality

In healthcare, web-based data entry has taken center stage regarding patient medical information.  Where in the past physicians and nurses entered information in paper charts, now there is a shift towards electronic health records (EHR).  By entering medical information during the encounter into an EHR, it mitigates recall bias and immediately makes the data available across treatment teams.

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Moreover, because EHR data is entered in a standard format, it allows hospital coders greater accuracy and precision when reporting performance and quality statistics at the state and federal levels.

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Parallel to the consolidation of processes reported by Benítez et al., healthcare has also streamlined parts of data analysis and visualization.  Federal entities such as the Agency for Healthcare Quality and Research (AHRQ) have developed algorithms that allow organizations to analyze information reported to the Centers for Medicare and Medicaid Services (CMS).  This is significant because all hospitals that treat Medicare or Medicaid patients must report this data to CMS.

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In healthcare, quality measures used to assess quality and performance are constantly under question and the criteria used to gather and assess social network data should face similar scrutiny (Glance, Osler, Mukamel, & Dick, 2008).

It is through the process of verifying each question and measure that we arrive at a standardized instrument that can be used by others.   Standardization allows comparisons to be made across studies over time developing the body of knowledge needed to arrive at insights and correlations (Cicchetti, 1994).

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With the rise of social network analysis, do you believe the standardization process will become more and more salient?

The need for standardization points towards the inherent need in social network analysis for high quality data to understand centrality.  Once we have the data, we must understand the importance of node centrality measures as they are able to realistically represent human interactions.  In one study, social networks among students of a karate class, dolphins, and the neural network of a nematode were analyzed.

Image result for zachary's karate clubImage result for node centrality closeness betweenness eigenvector

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The networks were studied for which measure of centrality really conveyed influence.  Their results identified key nodes as ones with high eigenvector and eccentricity centralities (Batool & Niazi, 2014).  Other factors of centrality considered include degree and closeness but these were not found to be significant.  The findings are consistent to our practicum exercise on crime networks as nodes high in eigenvector values were put forward as being high value targets.Image result for eigenvector centrality

The use of eigenvector values to understand influence is an intuitive one.  Alter nodes high in influence, power or resources being connected to an ego node confers power by associations.

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Eccentricity takes into account the maximal distance to other nodes from a central node  This is different from closeness because we are measuring how far a node is from one another, not how close.

Given the difficulty in realistically representing social networks, node centrality must be considered in the context of other factors.  For example, it was found in cases of complicated grief, node centrality and a node’s expected influence (EI) were both strongly correlated with network influence.  Here the EI was calculated as a measure of feelings of emptiness and emotional pain (Robinaugh, Millner, & McNally, 2016).  In fact, the EI was found to be more significant than node centrality in terms of influence.  It signifies the need to understand the data versus simply measuring centrality.

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The significance of EI points towards the work necessary in social network analysis to comprehensively explain behaviors and trends.  Node centrality is a strong starting point for understanding interactions, but the social network must be analyzed as a whole to explain observations.

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How could you practice beginner’s mind in social network analysis?

Bibliography

Batool, K., & Niazi, M. A. (2014). Towards a methodology for validation of centrality measures in complex networks. PLoS ONE. https://doi.org/10.1371/journal.pone.0090283

Benítez, J. A., Labra, J. E., Quiroga, E., Martín, V., García, I., Marqués-Sánchez, P., & Benavides, C. (2017). A Web-Based Tool for Automatic Data Collection, Curation, and Visualization of Complex Healthcare Survey Studies including Social Network Analysis. Computational and Mathematical Methods in Medicine. https://doi.org/10.1155/2017/2579848

Cicchetti, D. V. (1994). Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. Psychological Assessment. https://doi.org/10.1037/1040-3590.6.4.284

Glance, L. G., Osler, T. M., Mukamel, D. B., & Dick, A. W. (2008). Impact of the present-on-admission indicator on hospital quality measurement: Experience with the agency for healthcare research and quality (AHRQ) inpatient quality indicators. Medical Care. https://doi.org/10.1097/MLR.0b013e318158aed6

Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology. https://doi.org/10.1037/abn0000181

Blog 4: Social Capital

Social capital can be understood under the framework of a social network (Lin, 1999).   It is an “investment in social relations with expected returns.” At the same time, social capital must be measured using “embedded resources in social networks.”  Following this logic, for us to understand social capital, we must study how it is developed within a social network as a “network asset.”

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Two perspectives exist on how social capital should be measured.  The first involves a focus on the individual.  It asks what are the connections that people have and how do these connections provide an advantage, profit or return for the individual?  The second asks how connections can provide these beneficial effects at the group level in the form of “collective assets?”  In general connections within social networks provided access to greater resources and information allowing the receiver to be more fit than they would be otherwise.  We previously discussed how even weak ties can provide these advantages (Granovetter, 1973).

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However, what happens when people lack social capital and a means to obtain it?  To explore this situation, we need to consider an investigation into the lives of the “Black Urban Poor” (Smith, 2005).  It was found that for poor African-American, they lacked access to the social networks which over time could potentially increase their social capital.  Without even the possibility of the ability to obtain social capital via networking, it was extremely difficult for them to obtain the resources and information they need to find suitable employment.

Given the potential benefits of social capital, and the deleterious effects of doing without, what can be done to improve social capital within our society?  Efforts undertaken to improve social capital should be undertaken with caution.  In the past, similar efforts were attempted under different names and many have failed to produce positive results (Portes, 1998; Portes & Landolt, 1996).

Our readings point towards the potential benefit of social capital at the individual- and group-levels but does not provide a viable way to apply this to many of the problems that we see in society.  For example, in health care, there may be people with a great deal of social capital, but managers may be ineffective in leveraging this resource.  Under this context my research project will attempt to reconcile social capital, and creativity into the application of valuable, cost-effective programs within the hospital setting.

It is without question that many people on the board of trustees for hospitals have a great deal of social capital.  In many circumstances, they received these appointments after proving themselves over an extended and significant period.  However, this social capital is rarely used to push forward programs that would improve the delivery of health care.

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To help illustrate this point, consider the events that transpired after Cedar-Sinai Medical Center attempted to implement an Electronic Health Record (EHR) (Nembhard, Alexander, Hoff, & Ramanujam, 2009).  Without conferring with the hospitals staff and receiving their buy-in on how an EHR would improve efficiency and quality, an EHR was implemented.  Not surprisingly, after 1 week of use, most physicians got together and signed a petition to have the EHR removed.   What would you have done differently?  What did the implementation team fail to do?

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The initial step should have been to obtain buy-in from the board of trustees.  Clear evidence indicating the efficiencies garnered by EHR use could then cause these trustees with high social capital to influence staff further down the hierarchy.  Then as the consensus towards EHR use became evident to more staff, they should have been included into presentations that demonstrated the use and benefits of an EHR directly.  Thus, buy-in from trustees and staff physicians would have increased the social capital of the EHR over time providing more favorable conditions for success.  In addition to this, members of the board of trustees could have been connected through weaker ties to more distant physicians for their input.  This clustered group composition with the addition of weak ties allows for greater creativity to surface while maintaining the expertise of the core group.  Could this be applied to other industries?

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Consistent with the idea of social capital,  my small research project will attempt to study how the American Medical Association (AMA), and American Hospital Association (AHA)  attempt to exert their social capital by supporting politicians.  Specifically, the people that receive funds and their backgrounds will be put into context.  More so than the donation itself, knowing that the AMA and AHA place their support could have significant consequences.  Who are these people or organizations receiving support and is there wide variation in the size of donations?  How are their social networks structured?

 

 

Bibliography:
Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology. https://doi.org/10.1086/225469

Lin, N. (1999). Building a Network Theory of Social Capital. Connections. https://doi.org/10.1108/14691930410550381

Nembhard, I. M., Alexander, J. A., Hoff, T. J., & Ramanujam, R. (2009). Why Does the Quality of Health Care Continue to Lag? Insights from Management Research. Academy of Management Perspectives. https://doi.org/10.5465/AMP.2009.37008001

Portes, A. (1998). Social Capital: Its Origins and Applications in Modern Sociology. Annual Review of Sociology. https://doi.org/10.1146/annurev.soc.24.1.1

Portes, A., & Landolt, P. (1996). Unsolved mysteries: The Tocqueville files II: The downside of social capital. American Prospect.

Smith, S. S. (2005). “Don’t put my name on it”: Social Capital Activation and Job‐Finding Assistance among the Black Urban Poor. American Journal of Sociology. https://doi.org/10.1086/428814

Blog 3 – Node Centrality is a Small World Network

A quote from Bonnie Frickson, helps us to understand the effects of centrality, “I argue that interpersonal processes vary with the kind of larger structural unit within which individual ties are embedded.”  Not all central nodes are created equal and to provide context, different forms of centrality must be defined.

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Degree centrality: is related to the number of connections (degrees) that a central node has.   Degrees provide more sources of resources, and more connections to receive and disseminate information.   The more connections (friends) you have the more likely you are to pick up on helpful information. Information can garner power.  Many connections also provide you with more  options for acquiring a resource, increasing the power of the central node (buyer).

Eigenvector Centrality: deals with connections to high scoring, influential nodes.  The more highly regard the connected node, the more power is afforded to the central node.  This is consistent with power by association.

Betweenness Centrality: is a central node that provides the shortest path between nodes.  These nodes are powerful to the extent that needed information is conveyed between nodes.  This bridging of weak ties is consistent with the “strength” of weak ties.   (Granovetter, 1973)

Closeness Centrality: is measured by the closeness  of a central node to other nodes.  Friends that are very close friends are more likely to help one another out thus providing power to each individual friend.

How can this understanding of node centrality be applied?   They can shed light on the types of connections that form throughout nature and society.  The concept is embodied in small-world networks.  These networks maximize connectivity while minimizing the resources needed for connections.  The power that is garnered from different forms of node centrality take on the form of efficiency in small-world networks.

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Efficiency is increased as inefficient connections are pruned.  This process is termed cortical plasticity as explained by Nobel Prize Laureate Eric Kandel (Kandel et al, 2012).  In essence, the human brain is born with many neural connections, but only neural connections that are efficient and productive get activated and reinforced over time.  Cortical plasticity is greater during early childhood and explains the ability of children to pick up new languages and learn new musical instruments very quickly.  It also explains how adults can still learn new functions throughout their lives, because some degree of plasticity persists.

By analyzing these small-world networks people have already derived a great deal of efficiency in other contexts.  For example the small-world network organization of the Fibonacci sequence has been applied to break complex codes in cryptography (Baldonia, 2009) .   It could also be applied to the design computers that maximize performance using the technology we have available.   In what other areas can you see a small-world network being applied?

It is up to us as social network researchers to understand intimately the structural differences between nodes and connection in order to elucidate how small-world networks can be applied more broadly.  To help us get there we need “reliability and validity of network measurement,” as a foundation for more and more complex network analysis (Robins).

References:
Eric R. Kandel, MD; James H. Schwartz, MD, PhD; Thomas M., Principles of Neural Science, 2012

Maria Baldonia, Ciro Ciliberto, Giulia Cattaneo, Elementary Number Theory, Cryptography and Codes, 2009

Blog 2: Social Networks

The readings this week really helped me to look at networks from an enriched, more detailed perspective.   Research into networks deals with more than just personal attributes but with how people interact in relationships (Keim, 2011).

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The subset of lettered nodes in the picture above represent relationships that contribute to the whole.  The collection of these relationships provide us with social networks, where the whole is greater than the sum of its parts.  For example, it is the network that gives us social structure to understand what the norms of society may be.  Chances are, most of us follow these norms without even realizing because they are so ingrained into who we are.

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At a more basic level, relationships can be thought of in the context of information exchange (Haythornthwaite, 1996).  It is information between actors, or nodes that causes feelings of friendship,  belonging and love to develop.  This flow of information can determine new connections that form, and also can effect the network as a whole.

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Over time, the consequences of this flow can significant.  Take for example the idea of homophily (McPherson et al., 2001).  People have a natural tendency to group with those like themselves.  This can be seen in the relationships we have with friends, and in marriages.   Homophily allows for the possibility of isolation or cluster formation because of selective interaction.  As a consequence, perspectives can be narrower than they should be.  Consider what has happened to our political system as noted below. Image result for homophily

Contrary to generations past, homophily is making it more and more difficult to cross party lines.  The effects of homophily as our society becomes more and more interconnected is an opportunity for further research.  How will it affect our political system?

Social identity theory helps us understand the consequences of homophily.  As it is explained in the video, an ingroup and outgroup is formed causing differences between the groups to be amplified from bias and discrimination.  In effect, this causes the ingroup and outgroup to compete against one another rather than working together.  (A sad set of circumstances for our political system.)

The two textbooks assigned for reading this week helped me to understand the semantics behind the component parts and representations of networks and how they can be applied to research various social networks.   With my focus on health care administration, I especially appreciated how Robins used the example of snowball sampling to determine network boundaries.  This method was applied to determine if a relevant, representative  sample of decision makers was reached in the hospital setting.

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As more and more hospitals implement electronic health records (EHR) increasing inter-connectivity between patients and physicians, as well as among physicians, I am curious to find out how this will ultimately affect social networks in health care.  Will different departments started working more closely together? What are some of the long-term consequences.  As of yet there are mixed reviews regarding EHR usage.  This is also an area for future research.

 

Blog 1: Connected

Reading the book Connected by Christakis and Fowler was a very eye-opening experience.  Never before did I image how interconnected we were to those around us.  I had previously heard that we were all connected to one another with at most six degrees of separation, but unaware of the level or impact of these connection.  According to the third degree rule, we significantly affect one another through three degrees of separation, with the effect getting weaker the more distant the connection.  This means that a friend, a friend’s friend, and a friend’s friend’s friend will all affect one another in tangible ways.  This influence persists even if the more distant friends never interact with one another directly.  To highlight this point, as it pertains to the social network below, Liz can affect Allen and Allen can affect Lisa, even if Liz and Lisa never have direct contact, she can affect her through indirect connections.  Moreover, the connection is a two-way street.

Image result for social network nodes

While simplistic, this transfer of influence can persist on a larger scale depending on the distance of nodes from one another and transitivity (Christakis and Fowler 2010, Social Network Sensors for the Early Detection of Contagious Outbreaks).  Distant connections, ones with few mutual ties, can serve as bridges allowing influence to reach new networks of connections.  In the picture above this could be Allen’s connection to Liz, allowing him to affect the highly transitive (many mutual connections) network on the left.  Increased transitivity allows members of the network to influence one another more readily.  In the case of a pathogen, if Allen was a carrier of a virus and exposed Liz, Liz could infect Emma and Shane.  Once Emma and Shane are infected, because of their high transitivity, the rest of their network could be infected.  This allows us to understand how pockets of disease can form, similar to what happened in Rockdale, GA.

Detailed interview transcripts: https://www.pbs.org/wgbh/pages/frontline/shows/georgia/etc/synopsis.html

Video:
https://archive.org/details/FrontlineChildrenOfRockdaleCounty

network visualization of the outbreak

Other examples in the book used to illustrate connectivity include social network effects on patterns of obesity, voting, and smoking.

The insights gained from our study of social networks can be operationalized to help detect and prevent disease. Christakis and Fowler mentioned how vaccines could be targeted to highly connected individuals, at a central  hub of a network, to increase herd immunity versus immunizing the entire population.  This concept is shared by others. (Eubank et al.  2004, Modelling Disease Outbreaks in Realistic Urban Social Networks) Nodes that serve as a central hub can be used to detect the incidence of disease in a population. Targeting such individuals may also help in programs trying to change the behavior of a populations: criminals that regularly go in and out of jail could be more effectively persuaded to change their ways if people at the central hub demonstrated an aversion to crime or modified their own behavior.

Our discussion thus far has focused more on strong network connections but weaker connections also have a tremendous impact.  Weaker connections allow for the passage of information between transitive networks.  For this reason, weaker connections often are the source of new employment opportunity because they provide new information.  It has also been found through the study of the creators of musicals that a group with highly transitivity, coupled with more distant connections (providing different perspectives),  allows for the emergence of greater creativity as a whole increasing the  probability of success for the newly created musical.