A social network is a social structure made of nodes (which are generally individuals or organizations) that are tied by one or more specific types of interdependency, such as values, visions, ideas, financial exchange, friendship, kinship, dislike, conflict or trade. The resulting graph-based structures are often very complex.
Social network analysis 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. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.
In its simplest form, a social network is a map of all of the relevant ties between the nodes being studied. The network can also be used to determine the social capital of individual actors. These concepts are often displayed in a social network diagram, where nodes are the points and ties are the lines.
Social network analysis (related to network theory) has emerged as a key technique in modern sociology, anthropology, sociolinguistics, geography, social psychology, communication studies, information science, organizational studies, economics, and biology as well as a popular topic of speculation and study.
People have used the social network metaphor for over a century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In 1954, J. A. Barnes started using the term systematically to denote patterns of ties that cut across the concepts traditionally used by the public and social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as S.D. Berkowitz, Stephen Borgatti, Ronald Burt, Kathleen Carley, Katherine Faust, Linton Freeman, Mark Granovetter, David Knoke, Peter Marsden, Nicholas Mullins, Anatol Rapoport, Stanley Wasserman, Barry Wellman, Douglas R. White, and Harrison White expanded the use of social networks.
Social network analysis has now moved from being a suggestive metaphor to an analytic approach to a paradigm, with its own theoretical statements, methods, social network analysis software, and researchers. Analysts reason from whole to part; from structure to relation to individual; from behavior to attitude. They either study whole networks (also known as complete networks), all of the ties containing specified relations in a defined population, or personal networks, (also known as egocentric networks) the ties that specified people have, such as their "personal communities".
Several analytic tendencies distinguish social network analysis:
There is no assumption that groups are the building blocks of society: the approach is open to studying less-bounded social systems, from nonlocal communities to links among Web sites.
Rather than treating individuals (persons, organizations, states) as discrete units of analysis, it focuses on how the structure of ties affects individuals and their relationships.
In contrast to analyses that assume that socialization into norms determines behavior, network analysis looks to see the extent to which the structure and composition of ties affect norms.
The shape of a social network 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 (weak ties) to individuals outside the main network. More open networks, with many weak ties and social connections, are more likely to introduce new ideas and opportunities to their members than closed networks with many redundant ties. In other words, a group of friends who only do things with each other already share the same knowledge and opportunities. A group of individuals with connections to other social worlds is likely to have access to a wider range of information. It is better for individual success to have connections to a variety of networks rather than many connections within a single network. Similarly, individuals can exercise influence or act as brokers within their social networks by bridging two networks that are not directly linked (called filling structural holes).
The power of social network analysis stems from its difference from traditional social scientific studies, which assume that it is the attributes of individual actors—whether they are friendly or unfriendly, smart or dumb, etc.—that matter. Social network analysis produces an alternate view, where the attributes of individuals are less important than their relationships and ties with other actors within the network. This approach has turned out to be useful for explaining many real-world phenomena, but leaves less room for individual agency, the ability for individuals to influence their success, because so much of it rests within the structure of their network.
Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different organizations. For example, power within organizations often comes more from the degree to which an individual within a network is at the center of many relationships than actual job title. Social networks also play a key role in hiring, in business success, and in job performance. Networks provide ways for companies to gather information, deter competition, and collude in setting prices or policies.
KNOWLEDGE NETWORKING
Knowledge networking is the creation and development of knowledge through person-to-person networking, often augmented by online communications.
GENERAL DEFINITION
David J. Skyrme has based his definition among others on Naisbitt's work, he describes knowledge networking as “a phenomenon in which knowledge is shared, developed and evolved”, as a process of “human and computer networking where people share information, knowledge and experiences to develop new knowledge for handling new situations”. He sees knowledge networking as a different way of working where “it is about openness and collaboration across departmental, organizational and national boundaries and about building multiple relationships for mutual benefit.”
Gilbert Probst describes knowledge networks as follows: “Networks, by definition, connect everyone to everyone. Hierarchies by definition, do not; rather they create formal channels of communication and authority. Networks operate informally with few rules, they depend on trust.”
Marleen Huysman and Dirk deWit describe knowledge networking as "a collective acceptance of shared knowledge as being the key method of generating value to the organization."
Some systems theory driven authors see knowledge networks further as biological organisms which can take a variety of forms, and vary in how static and dynamic they are.
CHARACTERISTICS OF KNOWLEDGE NETWORKING
David J. Skyrme further describes key characteristics of knowledge networking:
Structural components: the network's nodes and links
Links provide paths for communications, knowledge flows and developing of personal relationships
Nodes in networks can be individuals or teams
The nodes are the focal points for activity or formal organizational processes
The pattern of nodes and links continually changes
The density of connections exhibits many forms – some may be more circular with obvious hubs; others may be more diffuse
Individuals belong to several networks – in some they are more central than in others
There is often no discernible boundary to a network
Networks connect to each other; links strengthen and weaken
One-to-one and multiple conversations take place; asynchronously or synchronously
Knowledge flows on both deliberate and unanticipated ways.
