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Chapter 1.13Social Networking

Kevin CurranUniversity of Ulster, UK

Paul O’KaneUniversity of Ulster, UK

Ryan McGinleyUniversity of Ulster, UK

Owen KellyUniversity of Ulster, UK

INTRODUCTION

It is in man’s nature to form communities, and it is also in his nature to communicate. Psychologists hold that man is moved by instincts, desires which can only find full satisfaction in a community and by communication. Social networking (or network theory) is not an exact science and may reasonably be termedasocialcatalyst indiscovering themethod in which problems are solved; organisations are run to the degree in which individuals succeed in achieving goals (Freeman, 2004). In the network theory, social relationships are discussed in terms of nodes and ties: the former individual actors, the latter, relationships within networks frequently described diagrammatically where the node is a

point, and the ties, lines of social connectivity(Scott, 2000).

Such social network diagrams can be used to measurethesocialcapitalof individualnodes/actors: a measurement, or determination of the usefulness of the network to the actors individually, as it is that measurement of usefulness to the individual which not only assesses the social capital of actors, but which by extension may shape and expose the very nature of the network as an entity. Loose con-nections (weak ties) reflect the greater possibility of openness in the network (Granovetter, 2003). This, in turn, is more likely to bring new ideas, new opportunities, and greater scope for innovation than close networks with many redundant ties. It is clear that “the friendly network” composed of friends already have common knowledge, com-mon interests, and common opportunities. Better DOI: 10.4018/978-1-60566-014-1.ch177

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still, it has access to wider social geographies. Again, the group with links to many networks has potentially greater access to other social arenas and a more extensive field of information, and thus the individuals, have links to a diversity of networks, as opposed to those within a single network, and can exercise more power and exact more influence by acting as brokers between their own and other networks not directly linked. This “polylinkage,” or “filling social holes,” places greater emphasis on the qualities or attributes of individuals.Theabilityof individuals to influence their success depends largely on the nature and structure of their network. Figure 1 illustrates a social network. Company A is a large fashion design house, a national company.

Company B imports and packs material for A’s use, but so far, A has little interest in a take over bid because of continuing government financial enhancements and certain tax concessions. Ahas, thus far, also ignored the lure of outsourcing to Asia, where it could control material at the point of manufacture. Company B imports most of the

material A requires, and supplies A at a mark up sufficient to meet all the transport costs. B is in “comfortable survival,” for as a condition of title to financialenhancements in an areaof high unem-ployment.This interactionwhenexaminedwithin the social network characterizes, not only inter-dependence that exists between the companies, but the in-group factor, and however “shocking” a statutory body for justifiable reasons, supports the “cosy” arrangement (Wellman & Berkowitz, 1988). That arrangement, in a very real sense, runs contrary to Sociometry, which attempts to quantify social relationships and which Gra-novetter explained in finding that, “Power within organisations” comes from an individual’s power within a network rather than the post or the title he or she holds (Granovetter, 1990). In the relatively simple example of companies Aand B, the power of each company is totally dependant on govern-ment legislation, which was arrived at as the result of a debate in the House and a vote in parliament. Self evidently, the individual within networks Aand B have little to do with the present state of

Figure 1. Social networking

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business. B depends on Aand Aon the legislation derived from a free vote in parliament.

However, in a strike or work to rule situation, it is the individual who holds the power. Granovet-ter, in the final analysis, appears to be correct, if and only if the cosy status quo continues. It is a basic law of Physics that, “Every action has an equal an opposite reaction,” and that law appears, so far, to hold true in Social Networking. There are, however, those who would claim that Social Networking or Network Theory is all theory, yet not really theoretical on account of too much methodology (Scott, 2000). The core problem with this stems from an apparent inability to test hypothesis in a mathematical way, that is, using statistics as the data by its very nature negates the randomsampling,whichstatisticsdemands.Here, even the computer and its resources do not appear as being capable of handling larger and larger databases, where networks expand. We present examples of social networking which integrate sociology and psychology within everyday life. In particular, we use examples relating to an or-ganisation’s internal structure, but this can also be extended further to university classes as well as the politics associated with any group in relation to sports teams and then with work and sport aside another example given to address this essay topic is the rise of social networking Web sites such as bebo.com.Asanoverviewtherewill alwaysbe the so called in-groups and out-groups, and so there willbe the inevitablegrouppoliticsassociatedwith the individuals involved. Social networking was first created in 1954 by “J.A. Barnes” (Barnes, 1954) where he talks about social circles relat-

ing to casual acquaintances or friends and these connections are important as they have a direct impact upon productivity and individual motiva-tion. Here, we concentrate on social networking in relation to analysis. The examples presented show how groups behave and how group politics affects everyone involved, whether it be working in a job or studying at university (Alexander & Danowski, 1990).

SOCIAL NETWORKING

The amount of information needed to describe even the smallest of social networks can be quite big. Tools from mathematics are used to help all of the tasks of social network methods (Newman, 2003). To help with the manipulation of network data and the calculation of indexes describing networks, matrices are very useful for recording information. An example of a simple matrix is shown in Figure 2.

The above matrix shows the structure of a close friendship in a group of four people: Ryan, Tara, Paul and Geraldine. It describes a pattern of liking ties with a point-to-point matrix where the rows represent choices by each actor. We put a “1” if a person likes another, and a “0” if they don’t. One reason for using mathematical and graphical techniques in social network analysis is to represent the descriptions of networks com-pactly and more efficiently. This also enables us to use computers to store and manipulate the information quickly and more accurately than we can by hand. The smaller, tighter networks are not

Figure 2. Matrix of group relationships

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as useful to their members as networks with lots of loose connections (weak ties) to individuals outsidethemainnetwork.This isbecausenetworks with weak ties are more likely to introduce new ideas and opportunities to their members than closed networks. For example, people who do things only with each other already share the same knowledgeandopportunities,whereaspeoplewith connections outside of each other are more likely to have access to a wider range of information. It is a lot more beneficial for individual success to have connections to a variety of networks rather than many connections with that of one network. Another advantage of having connections to a variety of networks is the use of filling social holes. This is when individuals can bridge two networks that are not directly linked to exercise influence or act as brokers within their social networks. Social network analysis (also known as network theory) has become a key technique in modern subjects such as:

• Sociology: The study of society and human social action, and includes the examination of the origins, institutions, organisation, and development of human life.

• Anthropology: The study of humanity. It is concerned with all humans at all times and with all dimensions of humanity.

• Social psychology: The study of how in-dividuals perceive, influence, and relate to others. The study of how our thought and self-awareness is social in origin.

• Organisational studies: Organisational behaviour, a distinct field of academic study which exams organisation through using the methods of economics, sociol-ogy, political science, anthropology, and psychology.

Social networks operate on many levels and play an important role in solving problems and howorganisationsare run, and ithelps individuals succeed in achieving their targets and goals. In So-

cial Network Theory, the attributes of individuals are less important than their relationships and ties with other points within the network (Newman, 2004). This approach both has its advantages and disadvantages. The advantage of this approach is that it is useful for explaining many real-world phenomena. The disadvantage, however, of this approach is that it leaves less room for individual agency, and the ability for individuals to influence their success because so much of it rests within the structure of the network. Social networks are also used toexaminehowcompanies interactwitheach other, as well as between individual employees at different companies. These networks provide ways forcompanies to:gather information, reduce competition, and cooperate with rival companies for their mutual benefit in setting prices and poli-cies. Social networking can refer to a category of Internet applications to help connect friends, business partners, or other individuals together using a variety of tools. These applications are known as online social networks and are becom-ing increasingly popular (Watts, 2004). Online social networks are a special network service. It is social software specifically focusing on the building and verifying of social networks for whatever purpose. Social networks play a major role in hiring, in business success for firms, and in job performance. Social network theory in the social sciences began with the urbanisation stud-ies of the “Manchester School.” A genuine social network is limited to about 150 members (Cross & Parker, 2004). This is sometimes known as the Dunbar number, which measures the cognitive limit to the number of individuals with whom any one person can maintain stable relationships. It is theorised in evolutionary psychology that the number may be some kind of limit of average human ability to recognize members and track emotional facts about all members of a group. The need to track “free riders” is important, as larger groups tend to more freely allow cheats and liars to succeed. Free Riders are points who use more than their fair share of resources, or shoulder less

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than a fair share of the costs of its production. To connect two random people anywhere in the world through a chain of social acquaintances is generally short. This idea gave rise to the famous phrase six degrees of separation, which is a theory that anyone on earth can be connected to any other person through a chain of acquaintances that has no more than four intermediaries. Research has shown that about five to seven degrees of separa-tion are sufficient for connecting any two people through the Internet (Hill & Dunbar, 2002).

In 1995, the first social networking Web site was set up called Classmates.com. It was not until 2001 that Web sites using the Circle of Friends social networks started appearing, and the popular-ity of these is still growing today. The most recent social networking site is bebo.com. It currently has over 20 million members world-wide and is a free service. Through Bebo.com you can search for friends, browse member homepages, learn more about people you see every day, write and draw on other people’s white boards, join “Clubs,” see events and parties on the calendar, keep in contact with friends at other schools and colleges, share photos privately or publicly, create quizzes about yourself, and blog. These social networks start out by an initial set of founders sending out a message inviting members of their own personal networks to join the site. The new members then repeat this process, growing the total number of members and links in the network. These sites then offer different features like viewable profiles, chat, and so forth. Social connections can also be used for business connections. Blended networking is an approach to social networking that combines both off-line elements (face-to-face events) and online elements. Social computing is the use of social software, which is based on creating or recreating social conversations and social contexts online through the use of software and technology. An exampleofsocialcomputingis theuseofe-mail for maintainingsocialrelationships(Carrington,Scott, & Wasserman, 2005). There are some indices for social network analysis, which are as follows:

• Betweeness: Measures the extent to which a particular point lies “between” the vari-ous other points in the graph. It is the most complex of the measures of point centrality to calculate. It is the number of people who a person is connected to indirectly through their direct links.

• Closeness: The shortest distances between each individual and every other person in the network. The people who have the shortest paths have the best visibility into what is happening in the network.

• Degree: The amount of ties to other points in the network. It measures network ac-tivity for a node by using the concept of degrees.

• Eigenvector centrality: Measures the importance of a node in the network. It assigns relative scores to all nodes in the network.

• Clustering coefficient: Measures the like-lihood that two associates of a node are as-sociates themselves. Clustering coefficient graphs measure to determine if a graph is a small-world network (a class of random graphs where most nodes are also neigh-bours of one another) or not.

• Cohesion: Measures how well the lines of source code within a module work together to provide a specific piece of functionality. It is expressed as either higher cohesion or low cohesion. The advantages of high cohesion are robustness, reliability, reus-ability, and understandability. The disad-vantages of low cohesion are difficult to maintain, difficult to test, difficult to reuse, and difficult to understand.

• Density: Individual-level density is the de-gree a respondents’ ties know one another. Network/global-level density is the number of ties in a network to the amount possible.

• Integration: Measures of group disper-sion or how network links focus on specific nodes.

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• Radiality: The degree in which a person’s network reaches out into the network and provides new information and influence.

• Reach: The manner which any member of a network can reach other members of the network.

• Structural equivalence: The extent to which nodes have a common set of linkag-es to other nodes in the system. The nodes do not need to have any linkages with each other to be structurally equivalent.

• Structural hole: These can be filled by connecting one or more links to link to-gether other nodes. Structural Hole is link-ing to ideas of social capital, for example, if you link two people who are not linked you can control their communication.

Asocial pyramid is a model of social relation-ships. Social intimacy is based on what layer of the pyramid you are on. The person with the least amount of social intimacy is placed at the foundation of the pyramid and the individual at the top of the pyramid has the highest amount of social intimacy.Sooneachsuccessive layergoing down, the individual has less and less intimacy. For example, a random person you interact with on the street is at the base of the pyramid, but your next of kin would be very close to the top. The philosophy of Social pyramids holds that the energy a person puts into the base of the pyramid is magnified at the top. For example, if a person gives positive energy to the people with whom they are at the base of their pyramid, it will be reflected in theirpersonal life.Viceversa, thesame can be said for the negative energy. Another type of social network is a sexual network, which is defined by the sexual relationships within a set of individuals. They can be formally studied using the mathematics of graph theory (Valente, 1996). Epidemiological studies (scientific study of fac-tors affecting the health and illness of individuals and populations) have researched into sexual networks, and have discovered that the statistical

properties of sexual networks are crucial to the spread of sexually-transmitted diseases (STDs). Social contract is a political theory that explains the basis and purpose of the state and of human rights. Within a society, all its members are as-sumed to agree to the terms and conditions of the social contract by their choice to stay within the society without violating the contract. The social safety net is a term used to describe a collection of services provided by the state (e.g., welfare, homelessshelters, etc.).Theyhelppreventanyone from falling into poverty beyond a certain level. An example of how the safety net works would be a single mother unable to work. She will receive benefits to the support the child so the child will have a better chance at becoming a successful member of society. Mathematical sociology is the usage of mathematics to draw up social theories. In sociology, the connection between mathemat-ics and sociology is limited to problems of data analysis. In mathematical sociology, the phrase “constructing a mathematical sociology” is used. This means making relevant assumptions about somemathematicalobjectsandprovidingpractical evaluations for ideas. It can also mean detecting properties of the model and comparing these with the relevant practical data.

NETWORK ANALYSIS

Thereare twokindsofSocialnetworkinganalyses offering two kinds of network data. These are Ego network analysis and complete network analysis. Ego network analysis questions respondents in the form of social survey, wherein each is asked about people they interact with and relationships within and between them (see Figure 3). Clearly, random sampling would be used, that is, from a large population, and thus Ego network analysis looks at and assesses the quality of each respon-dent’snetwork(size, income,age,etc).This typeof network analysis lends itself to random sampling, where statistics can be used to test the hypothesis.

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Complete network analysis deals with a set of re-spondents, for example, all employees in a given company,andtherelationshipsbetweenthemsuch as friendships and socialising (see Figure 4). The majority of research in Social network analysis is into complete networks, where centrality by way of subgroup examination is central and the only valid one.

Social networking involves the linked mea-surement of relationships between people and the product of their intellectual effort—knowledge/information. That knowledge must be seen as a “surrogate” who reflects the information released fromthemindsandmadeavailable for retrieval,or simplyviewingas thecircumstances require.This is truer than in our relationship with the computer, as an electro mechanical device which affords ac-

cess to and retrieval of desired information. The computeranditsoperatormaycorrectlybedeemed a single node, one part active and capable of direct-ing, theother, thecomputer, capableofobedience. Obedience in thiscontext is its capacity to respond to the primary nodes’ will, for example, read the work of others, study the nature and function of organisations, or to analyse a computer network and its topology. In the simplest of terms, the operator uses the computer to access particular information, or to contact a person or persons in order to gain information: a reciprocal operation (Carley & Newell, 1994).

People are used to find content, and content is used to find people, which is a reciprocal relation-ship, as illustrated in Figure 5.

Figure 3. Ego network

Figure 4. Complete network

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a. Bob (1.) asks his friend (2.) for information (strong tie but limited in information).

b. (2.) directs (1.) to search engine (3.) (again, a strong tie).

c. The information found here has been provided by others, the authors (4.) of the information (weak ties with unlimited information).

This situation reinforcesnetwork theory in that weak ties provide most information and strong ties provide limited information. Often the indus-trial application of social networking depends on “feedback” via hierarchical route and occasion-ally by way of random surveys. There have been instances when large corporations have altered policy, or procedures, or created innovations gathered from the minds of employees without those persons contribution being acknowledged. Such surveys have often produced new ideas to boast profits, or form the basic idea for a new product, capable of being produced more cheaply. So far, thecomputer/node links to information has been used as a simple social network example. There are, however, within computing in general and as a specialism, specific social networks where data is processed into a format useful to people and feeding “the information society,” groups of people who both generate and depend on information (Wasserman, 1994). The single

node/sole user consists of the combination of an animate and an inanimate node: the former the user, the latter the computer. At their interface exists almost infinite access to information and presuming the link with the ISP, the potential to become part of the network of networks, the Internet. If the operators, the animate node goes “online,” and are consciously or unconsciously engaging in the social anthropology of their own environment, their own social network, and that of the “global village.” In the context of network-ing, the node/operator has the choice of selecting strong ties with family and friends, but these limit diversity and the exploration of weak ties to the ever expanding realm of information. This node/operator, perhaps unwittingly, may enter the new world of “face to face” experiences of varied interaction (social computing), and while such social networking tends to recruit members from members, it is probable that the operator node will cooperate and enlarge the social site by inviting others from within a social network. These “introduction services” and similar social connections may be organised to include business connections.Theattractionof thesesocial sites lies in featuresoffered, suchasautomaticaddressbook updates, viewable updates, displayed feedback, and their introduction services with the potential to expand from sparse to dense social networks. It is a logical assertion that social networks are

Figure 5. Relationship between people and content

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prerequisite to the creation of valuable economics and computer linked communities, and because it is in our nature to socialise, maybe its time we demonstrated this in thesystemswedesign, for the success or failure of societies may well depend on the patterns of those social networking systems. Perhapsagoodpoint atwhich tobeginwouldbe to fullystudy,andhavingfully learned, try toachieve that level of social networking which already ex-ists in nature’s lower orders: ants, dolphins, and whales (all “wireless” of course).

SOCIAL NETWORKING EXAMPLES

Social networking analysis, according to Wikipe-dia.com, is defined as “The mapping and measur-ing of relationships and flows between people, groups,organisations,animals,computersorother information/knowledgeprocessingentities.”This in effect means that it relates to any network of people who are known as nodes. These nodes are connected to others via associations or connec-tions. These nodes or people connections have seven status types, which are as follows:

• Degree centrality: Social networking nodes relate to the degree concept. This is the number of node or people connections. This in English means the number of peo-ple that one individual knows within, say, a company or classroom or sports club. These connections can be significant and hence the following phrase of – “Having friends in higher places.”

• Betweeness centrality: This terminology relates to the node/ individual with a bridge role between two or more subgroups. Hence, there is communication flow be-tween the subgroups through the runner, so to speak, and this has its advantages and disadvantages.

• Closeness centrality: Individuals with this label in the network are in a good position

as they are in the loop in relation to infor-mation flow through a network. These peo-ple know individuals and subgroup’s both high and low, and because of there close-ness with everyone involved they get to see what is actually going on. (Hence Manager and Supervisor team).

• Network centralisation: This refers to the location of certain nodes within the net-work and relates to the network structure or hierarchy/chain of command and shows who reports to whom.

• Network reach: The reach here refers to the direct and indirect connection or con-tacts between different networks or social circles. For instance, in the case of two net-works, these are linked via the individual or nodes using direct and indirect links either directly between two individuals or subgroups linked through bridge nodes across the network.

• Peripheral players: These people are simply the dark horses within the network. Although these nodes are not directly con-nected to others, they still play a signifi-cant role in which a company would not exist without them. The classic example is a supplier of resources needed to create the overall product. The supplier works exter-nal to the organisation, but they are still val-ued by the company, as both must coexist to enable customer satisfaction through a high quality product being manufactured.

• Boundary spanners: These individuals are the key connectors to other subgroups and so these people integrate many people together and allow information transfer to flow better through more people within the network. These people are in a good posi-tion because their social circles are large, which enables them to swap ideas, and this information can be channelled effectively into quality goods and services.

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Social Networking Example

Figure 6 illustrates a model (based on a real-life experience by one of the article authors) of a uni-versity course where there are many subgroups, including the inferior subgroup and the superior subgroup, along with another subgroup that was indirectly connected to the inferior Subgroup.

• Degree centrality: The total number of connections within the “Inferior Subgroup” is 3, which is the same as the “Other Subgroup” that had a total of 3 connections also. But the “Superior Subgroup” has the most connections, with a total of 6.

• Betweeness centrality: There are 2 nodes that act as “Bridge Individuals” and they have the role as connectors of Subgroups. Hence, the “Superior Subgroup” was con-nected to the “Inferior Subgroup” through me, while the “Other Subgroup” was linked to the “Inferior Subgroup” through other nodes or “Bridge Individuals.”

• Closeness centrality: Although 2/3 of the subgroups appeared to be stable from the outside in, the “Inferior Subgroup” that consisted of a trio had barriers because 2/3 members were already friends and so they are known as “Clicks,” and therefore have a closer association compared to someone whose association with them is merely as

a social acquaintance. These, however, are the “Bridge Individuals” that connect sub-groups together so information flow can pass throughout the entire network of people.

• Network centralisation: There is no chain of command structure as such, but in relation to class hierarchy the Superior Subgroup” has the most numbers in there alliance, and so they monopolise the entire network because there is strength or power in numbers. Hence, they are positioned at the top end of the network, while the other subgroups are inferior and so lower down because it is the “Superior Subgroup” that has the best information flow between its larger alliance, and so they hold the most power within the network.

• Network reach: The “Superior Subgroup” is connected to the “Inferior Subgroup” through the nodes that act as “Bridge Individuals.” Also, the “Other Subgroup” is connected to the “Inferior Subgroup” through the “Bridge Individuals,” and so it is these bridge nodes that allow for Network Reach as they connect up the dif-ferent Subgroups and enable information to be passed through the network.

• Peripheral players: There are none in this social network because “Peripheral Players” refers to suppliers whose network is external to a company, although if any

Figure 6. University social network

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Sub-Group individual had connections with computer students higher up in the university, they could use these contacts to there advantage by seeking help from ad-vanced students and this information flow could benefit any Subgroup, but this is be-yond the scope of this article.

• Boundary Spanners: This basically re-lates to nodes that connect other Subgroups together to allow the passing of informa-tion between the networks, and hence the class network diagram has four Boundary Spanners or the two pairs of “Bridge Individuals.”

Here, we see that initially the number of stu-dents was large enough to fill a lecture theater. However,as thecourseprogressed,moreandmore students left, and as the numbers decreased, the social circles became more evident. Therefore, in subsequent years, the competition was even fiercer because the numbers were so small that every subgroup knew who each other was. With the smokescreen gone, the social politics were more serious, for the numbers were whittled down to the elite, those who really wanted to be there. Now that there were subgroups formed by individual students, thesegroupswouldhave their own status between the in-group. For example, when it comes to a group assignment, if the numbers are inadequate, then any individual may take the initiative to find someone to make up the numbers, but this is not always in the control of the group, for the lecturer may intervene to make up the numbers for an assignment, and if new subgroups are not formed, then existing groups are added, and these groups expand via the new individual who joins the subgroup, but the status battle continues. Thus, the ability for the group to gel as a whole may determine the outcome of the group assignment, for personality clashes may affect the team to work together as a whole, and so the big question is whether or not any new members get along with the established subgroup

ornot.The levelof successor failuremaygodown to the balance of power within the group, and the best way to explain this is by the common phrase of the word “Cliques,” for if members in a group are friends, then they are likely to stick together, and if they are in the majority they can control group activity to there own favor at the cost of minority individuals.

CONCLUSION

Social networks are social structures made up of nodes and ties. They indicate the relationships between individuals or organisations and how they are connected through social familiarities. They are very useful for visualising patterns. The use of mathematical and graphical techniques in social network analysis is important to represent the descriptions of networks compactly and more efficiently. They operate on many levels and play an important role in solving problems and on how organisations are run, and they help individuals succeed in achieving their targets and goals. In today’s society, social networks allow two people in different locations to interact with each other socially (e.g., chat, viewable photos, etc.) over a network. They are also very important for the social safetynetbecause this ishelping thesociety with the likes of the homeless or unemployed. Group politics relate to “In-Groups” and “Out-Groups,”aseachcompeteswitheachother.Social Networking is all around us and so there is always going to be friends and casual acquaintances, both within the subgroups and outside it. These status types link all subgroups together, as well as the internal structure of a group. Hence, there are direct and indirect connections to link everyone togetherwithinaworkplace,classroom,andsports club to online social circle Web sites like Bebo.com. Both these status types affect productivity, and so individual competition aside, success is determined by how well everyone involved can work toward a common goal.

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Barnes, J. A. (1954). Class and committees in a Norwegian Island parish. Human Relations, 7, 39. doi:10.1177/001872675400700102

Carley, K., & Newell, A. (1994). The nature of the social agent. The Journal of Mathematical Sociology, 19(4), 221–262.

Carrington, P., Scott, J., & Wasserman, S. (2005, February). Models and methods in social network analysis (Structuralanalysis in thesocialsciences). Cambridge University Press.

Cross, R., & Parker, R. (2004, June). The hidden power of social networks: Understanding how work really gets done in organizations. Harvard Business School Press.

Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of sci-ence. Vancouver: Empirical Press.

Granovetter, M. (1990). The myth of social net-work analysis as a special method in the social sciences. Connections, 13(2), 13–16.

Granovetter, M. (2003, August 8). Ignorance, knowledge and outcomes in a small world. Science, 301, 773–774. doi:10.1126/science.1088508

Hill, R., & Dunbar, R. (2002). Social network size in humans. Human Nature (Hawthorne, N.Y.), 14(1), 53–72. doi:10.1007/s12110-003-1016-y

Newman, M. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256. doi:10.1137/S003614450342480

Newman, M. (2004). Detecting community struc-ture in networks. European Physics Journal, 38, 321–330.

Scott, J. (2000, March). Social network analysis: A handbook (2nd ed.). SAGE Publications.

Valente, T.W. (1996, January). Social network thresholds in the diffusion of innovations. Social Networks, 18(1), 69-89(21).

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KEY TERMS AND DEFINITIONS

Betweeness: Measures the extent to which a particular point lies “between” the various other points in the graph. It is the most complex of the measures of point centrality to calculate. It is the number of people who a person is connected to indirectly through their direct links.

Closeness: The shortest distance between each individual and every other person in the network. The people who have the shortest paths have the best visibility into what is happening in the network.

Degree: The amount of ties to other points in the network. It measures network activity for a node by using the concept of degrees.

Cohesion: Cohesion measures how well the linesofsourcecodewithinamoduleworktogether to provide a specific piece of functionality. It is expressed as either higher cohesion or low cohe-sion. The advantages of high cohesion are robust-ness, reliability, reusability,andunderstandability. The disadvantages of low cohesion are difficult to

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maintain, difficult to test, difficult to reuse, and difficult to understand.

Network Density: Individual-level density is the degree a respondent’s ties know one another. Network/global-leveldensity is thenumberof ties in a network to the amount possible.

Network Integration: This measures a group dispersion or how network links focus on a spe-cific nodes.

Network Shape: The shape of the social net-work helps determine a network’s usefulness to its individuals. Smaller, tighter networks can be less useful to their members than networks with lots of loose connections to individuals outside the main network.

Radiality: The degree in which a person’s networkreachesout into thenetworkandprovides new information and influence.

Reach: The manner which any member of a network can reach other members of the net-work.

Social Network:Anetworkisasocialstructure made of nodes, which are generally individuals

or organizations. It indicates the ways in which they are connected through various social fa-miliarities, ranging from casual acquaintance to close familial bonds. The maximum size of social networks tends to be around 150 people and the average size around 124. Social network theory views social relationships in terms of nodes and ties. Nodes are the individual actors within the networks, and ties are the relationships between the actors..

Structural Equivalence:The extent to which nodes have a common set of linkages to other nodes in the system. The nodes do not need to have any linkages with each other to be structur-ally equivalent.

Structural Hole: These can be filled by connecting one or more links to link together other nodes. Structural Hole is linking to ideas of social capital, for example, if you link two people who are not linked, you can control their communication.