Question

1. What are the different areas of application of social network analysis? Mention at least 5 areas with one application in each area.
2. What are the different levels of social network analysis? What is the difference between them?
3. Why do we need a different level of social network analysis?
4. What is a clique? What are the advantages and disadvantages of a clique?
5. What is the difference between K-plex and K-core?

Answer 1

Security applications

Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map covert organizations such as an espionage ring, an organized crime family or a street gang. The National Security Agency (NSA) uses its electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis.^[57] After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network.^[58] This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network. The NSA has been performing social network analysis on call detail records (CDRs), also known as metadata, since shortly after the September 11 attacks.^[59][60]

Textual analysis applications

Large textual corpora can be turned into networks and then analysed with the method of social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated by using parsers. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.^[61] This automates the approach introduced by Quantitative Narrative Analysis,^[62] whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.^[63]

In other approaches, textual analysis is carried out considering the network of words co-occurring in a text (see for example the Semantic Brand Score). In these networks, nodes are words and links among them are weighted based on their frequency of co-occurrence (within a specific maximum range).

Internet applications

Social network analysis has also been applied to understanding online behavior by individuals, organizations, and between websites.^[15] Hyperlink analysis can be used to analyze the connections between websites or webpages to examine how information flows as individuals navigate the web.^[64] The connections between organizations has been analyzed via hyperlink analysis to examine which organizations within an issue community.^[65]

Social Media Internet Applications

Social network analysis has been applied to social media as a tool to understand behavior between individuals or organizations through their linkages on social media websites such as Twitter and Facebook.^[66]

In computer-supported collaborative learning

One of the most current methods of the application of SNA is to the study of computer-supported collaborative learning (CSCL). When applied to CSCL, SNA is used to help understand how learners collaborate in terms of amount, frequency, and length, as well as the quality, topic, and strategies of communication.^[67] Additionally, SNA can focus on specific aspects of the network connection, or the entire network as a whole. It uses graphical representations, written representations, and data representations to help examine the connections within a CSCL network.^[67] When applying SNA to a CSCL environment the interactions of the participants are treated as a social network. The focus of the analysis is on the "connections" made among the participants – how they interact and communicate – as opposed to how each participant behaved on his or her own.

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Although levels of analysis are not necessarily mutually exclusive, there are three general levels into which networks may fall: micro-level, meso-level, and macro-level.

                                                                           Micro level

At the micro-level, social network research typically begins with an individual, snowballing as social relationships are traced, or may begin with a small group of individuals in a particular social context.

Dyadic level: A dyad is a social relationship between two individuals. Network research on dyads may concentrate on structure of the relationship (e.g. multiplexity, strength), social equality, and tendencies toward reciprocity/mutuality.

Triadic level: Add one individual to a dyad, and you have a triad. Research at this level may concentrate on factors such as balance and transitivity, as well as social equality and tendencies toward reciprocity/mutuality.^[35] In the balance theory of Fritz Heider the triad is the key to social dynamics. The discord in a rivalrous love triangle is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of signed graphs.

Actor level: The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, centrality, prestige and roles such as isolates, liaisons, and bridges.^[37] Such analyses, are most commonly used in the fields of psychology or social psychology, ethnographic kinship analysis or other genealogical studies of relationships between individuals.

Subset level: Subset levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on distance and reachability, cliques, cohesive subgroups, or other group actions or behavior.

                                                                                       Meso level

In general, meso-level theories begin with a population size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.^[39]

Organizations: Formal organizations are social groups that distribute tasks for a collective goal.^[40] Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of formal or informal relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments

Randomly distributed networks: Exponential random graph models of social networks became state-of-the-art methods of social network analysis in the 1980s.

Scale-free networks: A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. In network theory a scale-free ideal network is a random network with a degree distribution that unravels the size distribution of social groups.

                                                                                        Macro level

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as economic or other resource transfer interactions over a large population.

Large-scale networks: Large-scale network is a term somewhat synonymous with "macro-level" as used, primarily, in social and behavioral sciences, in economics

Complex networks: Most larger social networks display features of social complexity, which involves substantial non-trivial features of network topology, with patterns of complex connections between elements that are neither purely regular nor purely random (see, complexity science, dynamical system and chaos theory), as do biological, and technological networks.

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In general, social networks are self-organizing, emergent, and complex, such that a globally coherent pattern appears from the local interaction of the elements that make up the system.^[32][33] These patterns become more apparent as network size increases. However, a global network analysis^[34] of, for example, all interpersonal relationships in the world is not feasible and is likely to contain so much information as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis.^[35][36] The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question.

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in the social sciences, is a group of individuals who interact with one another and share similar interests. Interacting with cliques is part of normative social development regardless of gender, ethnicity or popularity.

positive effects of cliques

Forming groups of like individuals can benefit said individuals immensely. Creating bonds with people can help develop necessary social skills like collaboration and conflict resolution. Also, a group of genuine friends that have similar interests can be a positive influence on one's behavior. They can help you try new things, boost your self-confidence, and help you develop your identity. In addition to that, a good effect of social groups is it fulfills that need to belong without negatively impinging upon your decisions.

negative effects of cliques

Developing social groups and cliques, while sometimes beneficial, can have many consequences. The lack of socialization between social groups makes people less prepared for making friends during their adulthood. Cliques also put labels and stereotypes on groups of people, forcing people's true identities to remain hidden. In addition to this, the attributes that kids are rejected for are often the ones most respected outside of school and into adulthood. If these attributes are smothered by conformity, it will be difficult to form an identity.

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