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Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models, and applications that are expressed in terms of relational concepts or processes. Along with growing interest and increased use of network analysis has come a consensus about the central principles underlying the network perspective. In addition to the use of relational concepts, we note the following as being important:
Actors and their actions are viewed as interdependent rather than independent, autonomous units Relational ties (linkages) between actors are channels for transfer or "flow" of resources (either material or nonmaterial) Network models focusing on individuals view the network structural environment as providing opportunities for or constraints on individual action Network models conceptualize structure (social, economic, political, and so forth) as lasting patterns of relations among actors
The unit of analysis in network analysis is not the individual, but an entity consisting of a collection of individuals and the linkages among them. Network methods focus on dyads (two actors and their ties), triads (three actors and their ties) or larger systems (subgroups of individuals, or entire networks.
Actor/Node/Point/Agent: social entities such as persons, organizations, cities, etc. Tie/Link/Edge/Line/Arc: represents relationships among actors. Dyad: consists of a pair of actors and the (possible) tie(s) between them. Triad: a subset of three actors and the (possible) tie(s) among them. Subgroup: subset of actors and all ties among them. Group: collection of all actors on which ties are to be measured. Relation: collection of ties of a specific kind among members of a group. Social Network: finite set or sets of actors and the relation or relations defined on them. Social networks can be represented as GRAPHS or MATRICES. Wasserman, S. and K. Faust, 1994, Social Network Analysis. Cambridge: Cambridge University Press.
A technique to map and study the relationships between people or groups. The basic concept of the social network is familiar to anyone who has used Friendster or played Six Degrees of Kevin Bacon. Social network analysis formalizes this parlor game, using details about the network to interpret the role of each person or group.
2. How does it work?
"Conventional" social science data consist of a rectangular array of measurements. The rows of the array are the cases, or subjects, or observations. The columns consist of scores (quantitative or qualitative) on attributes, or variables, or measures.
We could look at this data structure the same way as with attribute data. By comparing rows of the array, we can see which actors are similar to which other actors in whom they choose. By looking at the columns, we can see who is similar to whom in terms of being chosen by others. These are useful ways to look at the data, because they help us to see which actors have similar positions in the network. This is the first major emphasis of network analysis: seeing how actors are located or "embedded" in the overall network.
The major difference between conventional and network data is that conventional data focuses on actors and attributes; network data focus on actors and relations. The difference in emphasis is consequential for the choices that a researcher must make in deciding on research design, in conducting sampling, developing measurement, and handling the resulting data. It is not that the research tools used by network analysts are different from those of other social scientists (they mostly are not). But the special purposes and emphases of network research do call for some different considerations.
Covert networks often don't behave like normal social networks (Baker and Faulkner, 1993). Conspirators don't form many ties outside of their immediate cluster and often minimize the activation of existing ties inside the network. Strong ties between prior contacts, which were frequently formed years ago in school and training camps, keep the cells linked. Yet, unlike normal social networks, these strong ties remain mostly dormant and therefore hidden to outsiders.
To draw an accurate picture of a covert network, we need to identify task and trust ties between the conspirators. The best solution for network disruption may be to discover possible suspects and then, via snowball sampling, map their individual personal networks - see whom else they lead to, and where they overlap. To find these suspects it appears that the best method is for diverse intelligence agencies to aggregate their individual information into a larger emergent map. By sharing information and knowledge, a more complete picture of possible danger can be drawn. In my data search I came across many news accounts where one agency, or country, had data that another would have found very useful. To win this fight against terrorism it appears that the good guys have to build a better information and knowledge sharing network than the bad guys (Ronfeldt and Arquilla, 2001).
The Semantic Web is an evolving development of the World Wide Web in which the meaning (semantics) of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. It involves publishing in languages specifically designed for data: Resource Description Framework (RDF), Web Ontology Language (OWL), and Extensible Markup Language (XML).
A popular application of the semantic web is Friend of a Friend (or FoaF), which uses RDF to describe the relationships people have to other people and the "things" around them. FOAF permits intelligent agents to make sense of the thousands of connections people have with each other, their jobs and the items important to their lives; connections that may or may not be enumerated in searches using traditional web search engines. Because the connections are so vast in number, human interpretation of the information may not be the best way of analyzing them.
FOAF is an example of how the Semantic Web attempts to make use of the relationships within a social context.
Whatever metaphor is applied to untangling terrorist activity, the Semantic Web can be a useful technology, for managing and analyzing data about terrorist activities. Terror operations are conspiracies involving a small group of people carrying out a complex chain of actions and bound by an intricate web of relationships. The Semantic Web can serve as a valuable tool for gathering, organizing, and disseminating information.
Mapping a terrorist organization or event requires a series of steps: gathering data, organizing the data and outlining connections, and identifying holes in the connections and developing theories to fill them. The process is then repeated as theories are tested and new leads are generated. In this process, enormous amounts of seemingly irrelevant data is accumulated, but it will need to be organized into the framework as well because it may become relevant in a later stage of the investigation. The Semantic Web can be a useful tool at each stage of an investigation.
By conducting smart searches, the Semantic Web can surmount these weaknesses and maximize the amount of relevant data brought to the intelligence analyst's attention.
Unlike search engine which is based on natural language, is simply matching characters the information has no inherent meaning to the search engine. For the Semantic Web, Zawahiri and 1993 would have a specific meaning, so that a search could focus on information relating to Zawahiri's activities in the appropriate timeframe. The marked-up data could then be pulled into the researcher's Semantic Web portal automatically. This ability to aggregate information could save the researcher hours of scanning documents, and, by automatically placing information into a context, possibly reveal connections that the researcher would not have noticed.
A Semantic Web portal could be encoded to recognize Osama ben Laden and Usama bin Ladin as the same person- or to compare other information, such as birth date or nationality. A Semantic Web portal could also be encoded to recognize nicknames and aliases, for example Abu Ammar as a common nickname for Yasser Arafat. The Semantic Web also gives the user the ability to shape the information according to changing needs. A Semantic Web portal can be used to examine information from several different angles. This flexibility in structuring the data is particularly useful for tracking the movement of money and of suspected terrorists.