“I was never interested in Facebook or MySpace because they feel like malls to me. Twitter actually feels like the street. You can bump into anybody on Twitter.” — Science-fiction novelist William Gibson – New York (October 11, 2010)
“There is almost certainly an evolutionary drive toward increasing complexity in the face of entropy. That’s practically a definition of life. Technology is so powerful and attractive to us because it holds the promise of greater complexity and greater connectedness. Atoms to molecules to cells to organelles to organisms. What’s next? No one knows for sure, but it sure ain’t Facebook.” — American media theorist Douglas Rushkoff, writer, columnist, early cyberpunk culture adopter, and advocacy of open source solutions to social problems.
Precursors of social networks in the late 1800s include Émile Durkheim and Ferdinand Tönnies. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (gemeinschaft) or impersonal, formal, and instrumental social links (gesellschaft) [in Linton Freeman, “The Development of Social Network Analysis“, Vancouver, Empirical Press, 2004 (here is a valuable must-read review on it)]. Durkheim gave a non-individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. He distinguished between a traditional society – “mechanical solidarity” – which prevails if individual differences are minimized, and the modern society – “organic solidarity” – that develops out of cooperation between differentiated individuals with independent roles. Then, Georg Simmel (1908-1971), writing at the turn of the twentieth century, was the first scholar to think directly in social network terms. His essays pointed to the nature of network size on interaction and to the likelihood of interaction in ramified, loosely-knit networks rather than groups.
Nowadays, however, the paraphernalia of increasing intelligent tools (network metrics) are widely available, mainly to the exponential role of the science of complex networks. As stated by Matthias Scholz (Network Science.org / Webpage) (…) Network science has received a major boost caused by the widespread availability of huge network data resources in the last years. One of the most surprising findings, popularized by Albert-László Barabási and his team, is that real networks behave very distinct from traditional assumptions of network theory. Traditionally, real networks were supposed to have a majority of nodes of about the same number of connections around an average. This is typically modelled by random graphs. However, modern network research revealed that the majority of nodes of real networks is very low connected, and, by contrast, there exists some nodes of very extreme connectivity (hubs). This power-law characteristics, termed scale-free by Barabási, can be found in many complex real networks from biological (natural) to social man-made networks (…).
While embedding themselves on social-networking, people do tend to forget this, of course, but here are 2 or 3 things you should know about Social Networks before stupefying registering yourself on FarmVille (actually, this is a limited list of some of the actual metrics usually employed on current network analysis provided with a short description – So, … do really ponder yourself where you are – on the street or in the mall!):
Betweenness (link) The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node’s neighbours, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links. | Bridge (link) An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph. | Centrality (link) This measure gives a rough indication of the social power of a node based on how well they “connect” the network. “Betweenness”, “Closeness”, and “Degree” are all measures of centrality. | Centralization (link) The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses. | Closeness (link) The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. The shortest path may also be known as the “geodesic distance”. | Clustering coefficient (link) A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater ‘cliquishness’. | Cohesion (link) The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted. | …
Fig. – Hue (from red=0 to blue=max) shows the node betweenness. (link)
… Degree (link) The count of the number of ties to other actors in the network. See also degree (graph theory). | (Individual-level) Density (link) The degree a respondent’s ties know one another/ proportion of ties among an individual’s nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks). | Flow betweenness centrality (link) The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node). | Eigenvector centrality (link) A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question. | Local Bridge (link) An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle. | Path Length (link) The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes. | Prestige (link) In a directed graph prestige is the term used to describe a node’s centrality. “Degree Prestige”, “Proximity Prestige”, and “Status Prestige” are all measures of Prestige. See also degree (graph theory). | Radiality (link) Degree an individual’s network reaches out into the network and provides novel information and influence. | Reach (link) The degree any member of a network can reach other members of the network. | Structural cohesion (link) The minimum number of members who, if removed from a group, would disconnect the group. | Structural equivalence (link) Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent. | Structural hole (link) Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.
Of course, some of these metrics are redundant over each other, and in fact there is some intelligence on having this on-purpose redundancy. But I would say that Path Length and Cliqueness, Eigenvector centrality, Betweenness, Clustering coefficient, Degree and last but not least Flow (btw, here‘s my own poetic lateral view of it), would be the most important of them all, even if, these all depends on what you are, on what you see, on what you feel, and mostly on what you somehow rather expect from it as a whole experience over time. So, finally, let me just add that all these metrics will not avoid people from writing/sharing experiences like: “Son las 7 y 30 y estoy cagando” followed by “Son las 5 y 31 y ya cagado“. Unfortunately, what follows is precisely a video parody on Facebook not far from his own reality. As usual, you are always free to choose from the geriatric-like shelter malls or the open-air streets…