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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, idea, financial exchange, friends, kinship, dislike, conflict, trade, web links, sexual relations, disease transmission (epidemiology), or airline routes. The resulting 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, Linton Freeman, Mark Granovetter, Peter Marsden, Nicholas Mullins, Anatol Rapoport, Stanley Wasserman, Barry Wellman, 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 and research tribes. Analysts reason from whole to part; from structure to relation to individual; from behavior to attitude. They either study whole networks, all of the ties containing specified relations in a defined population, or personal 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.

In history,

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). 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.

Georg Simmel, 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 (Simmel, 1908/1971).

After a hiatus in the first decades of the twentieth century, three main traditions in social networks appeared. In the 1930s, J.L. Moreno pioneered the systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (sociometry), while a Harvard group led by W. Lloyd Warner and Elton Mayo explored interpersonal relations at work. In 1940, A.R. Radcliffe-Brown’s presidential address to British anthropologists urged the systematic study of networks.[2] However, it took about 15 years before this call was followed-up systematically.

Social network analysis developed with the kinship studies of Elizabeth Bott in England in the 1950s and the 1950s-1960s urbanization studies of the University of Manchester group of anthropologists (centered around Max Gluckman and later J. Clyde Mitchell) investigating community networks in southern Africa, India and the United Kingdom. Concomittantly, British anthropologist S.F. Nadel codified a theory of social structure that was influential in later network analysis.[3]

In the 1960s-1970s, a growing number of scholars worked to combine the different tracks and traditions. One large group was centered around Harrison White and his students at Harvard University: Ivan Chase, Bonnie Erickson, Harriet Friedmann, Mark Granovetter, Nancy Howell, Joel Levine, Nicholas Mullins, John Padgett, Michael Schwartz and Barry Wellman. White’s group thought of themselves as rebelling against the reigning structural-functionalist orthodoxy of then-dominant Harvard sociologist Talcott Parsons, leading them to devalue concerns with symbols, values, norms and culture. They also were opposed to the methodological individualism espoused by another Harvard sociologist, George Homans, which was endemic among the dominant survey researchers and positivists of the time. Mark Granovetter and Barry Wellman are among the former students of White who have elaborated and popularized social network analysis. [4]

White’s was not the only group. Significant independent work was done by scholars elsewhere: University of California Irvine social scientists interested in mathematical applications, centered around Linton Freeman, including John Boyd, Susan Freeman, Kathryn Faust, A. Kimball Romney and Douglas White); quantitative analysts at the University of Chicago, including Joseph Galaskiewicz, Wendy Griswold, Edward Laumann, Peter Marsden, Martina Morris, and John Padgett; and communication scholars at Michigan State University, including Nan Lin and Everett Rogers. A substantively-oriented University of Toronto sociology group developed in the 1970s, centered on former students of Harrison White: S.D. Berkowitz, Harriet Friedmann, Nancy Leslie Howard, Nancy Howell, Lorne Tepperman and Barry Wellman, and also including noted modeler and game theorist Anatol Rapoport. [5]

Applications:

SNA and network modeling approaches have been used in epidemiology to help understand how patterns of human contact aid or inhibit the spread of diseases such as HIV in a population. The evolution of social networks can sometimes be modeled by the use of agent based models, providing insight into the interplay between communication rules, rumor spreading and social structure. Here is an interactive model of rumour spreading, based on rumour spreading from model on Cmol.

Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices. Change agents and opinion leaders often play major roles in spurring the adoption of innovations, although factors inherent to the innovations also play a role.

Dunbar’s number: The rule of 150 suggested that the typical size of a social network is constrained to about 150 members due to possible limits in the capacity of the human communication channel. The rule arises from cross-cultural studies in sociology and especially anthropology of the maximum size of a village (in modern parlance most reasonably understood as an ecovillage). It is theorized 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. However, it may be due to economics and the need to track “free riders“, as it may be easier in larger groups to take advantage of the benefits of living in a community without contributing to those benefits.

Nevertheless, even as an average person may only be able to establish a few strong ties due to possible constraints of human communication channels, Mark Granovetter found in one study that more numerous weak ties can be important in seeking information and innovation. Cliques have a tendency to more homogeneous opinions as well as sharing many common traits. This homophillic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called the “the strength of weak ties”.

Guanxi is a central concept in Chinese society (and other East Asian cultures) that can be summarized as the use of personal influence. Guanxi can be studied from a social network approach.[6]

The small world phenomenon is the hypothesis that the chain of social acquaintances required to connect one arbitrary person to another arbitrary person anywhere in the world is generally short. The concept gave rise to the famous phrase six degrees of separation after a 1967 small world experiment by psychologist Stanley Milgram. In Milgram’s experiment, a sample of US individuals were asked to reach a particular target person by passing a message along a chain of acquaintances. The average length of successful chains turned out to be about five intermediaries or six separation steps (the majority of chains in that study actually failed to complete). The methods (and ethics as well) of Milgram’s experiment was later questioned by an American scholar, and some further research to replicate Milgram’s findings had found that the degrees of connection needed could be higher.[7] Academic researchers continue to explore this phenomenon as Internet-based communication technology has supplemented the phone and postal systems available during the times of Milgram. A recent electronic small world experiment at Columbia University found that about five to seven degrees of separation are sufficient for connecting any two people through e-mail.[8]

The study of socio-technical systems is loosely linked to social network analysis, and looks at relations among individuals, institutions, objects and technologies.

Metrics:

Betweenness
Degree an individual lies between other individuals in the network; the extent to which a node is directly connected only to those other nodes that are not directly connected to each other; an intermediary; liaisons; bridges. Therefore, it’s the number of people who a person is connected to indirectly through their direct links.
Closeness
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.
(Degree) centrality
The count of the number of ties to other actors in the network. See also degree (graph theory).
Flow betweenness centrality
The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node).
Eigenvector centrality
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.
Centralization
The difference between the n 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 n of links each node possesses
Clustering coefficient
A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater ‘cliquishness’.
Cohesion
The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every actor is directly tied to every other actor, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.
(Individual-level) density
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).
Path Length
The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes.
Radiality
Degree an individual’s network reaches out into the network and provides novel information and influence
Reach
The degree any member of a network can reach other members of the network.
Structural cohesion
The minimum number of members who, if removed from a group, would disconnect the group.[10]
Structural equivalence
Refers to the extent to which actors have a common set of linkages to other actors in the system. The actors don’t need to have any ties to each other to be structurally equivalent.
Structural hole
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.

In organizational development, socio-technical systems (or STS) is an approach to complex organizational work design that recognizes the interaction between people and technology in workplaces.

The term also refers to the interaction between society’s complex infrastructures and human behaviour. In this sense, society itself, and most of its sub-structures, are complex socio-technical systems.

The term sociotechnical systems was coined in the 1960s by Eric Trist and Fred Emery, who were working as consultants at the Tavistock Institute in London.

Major topics in social-technical systems are job design, job enrichment, job enlargement, job rotation, motivation, process improvement, satisfaction, task analysis, and self-managing teams.

[edit] Work design

Work design or job design in organizational development is the application of socio-technical systems principles and techniques to the humanization of work. The aims of work design to improved job satisfaction, to improved through-put, to improved quality and to reduced employee problems, e.g., grievances, absenteeism.

[edit] Job enrichment

Job enrichment in organizational development, human resources management, and organizational behavior, is the process of giving the employee a wider and higher level scope of responsibilitiy with increased decision making authority. This is the opposite of job enlargement, which simply would not involve greater authority. Instead, it will only have an increased number of duties.[1]

[edit] Job enlargement

Job enlargement means increasing the scope of a job through extending the range of its job duties and responsibilities. This contradicts the principles of specialisation and the division of labour whereby work is divided into small units, each of which is performed repetitively by an individual worker. Some motivational theories suggest that the boredom and alienation caused by the division of labour can actually cause efficiency to fall.

[edit] Job rotation

Job rotation is an approach to management development, where an individual is moved through a schedule of assignments designed to give him or her a breadth of exposure to the entire operation. Job rotation is also practiced to allow qualified employees to gain more insights into the processes of a company and to increase job satisfaction through job variation. The term job rotation can also mean the scheduled exchange of persons in offices, especially in public offices, prior to the end of incumbency or the legislative period. This has been practiced by the German green party for some time but has been discontinued

[edit] Motivation

Motivation in psychology refers to the initiation, direction, intensity and persistence of behavior.[2] Motivation is a temporal and dynamic state that should not be confused with personality or emotion. Motivation is having the desire and willingness to do something. A motivated person can be reaching for a long-term goal such as becoming a professional writer or a more short-term goal like learning how to spell a particular word. Personality invariably refers to more or less permanent characteristics of an individual’s state of being (e.g., shy,extrovert, conscientious. As opposed to motivation, emotion refers to temporal states that do not immediately link to behavior (e.g., anger, grief, happiness).

[edit] Process improvement

Process improvement in organizational development is a series of actions taken to identify, analyze and improve existing processes within an organization to meet new goals and objectives. These actions often follow a specific methodology or strategy to create successful results.

[edit] Task analysis

Task analysis is the analysis of how a task is accomplished, including a detailed description of both manual and mental activities, task and element durations, task frequency, task allocation, task complexity, environmental conditions, necessary clothing and equipment, and any other unique factors involved in or required for one or more people to perform a given task. This information can then be used for many purposes, such as personnel selection and training, tool or equipment design, procedure design (e.g., design of checklists or decision support systems) and automation.

Problem Statement:

There are an increasing number of new “social applications” as well as traditional application which either require the “social graph” or that could provide better value to users by utilizing information in the social graph. What I mean by “social graph” is a the global mapping of everybody and how they’re related, as Wikipedia describes and I talk about in more detail later. Unfortunately, there doesn’t exist a single social graph (or even multiple which interoperate) that’s comprehensive and decentralized. Rather, there exists hundreds of disperse social graphs, most of dubious quality and many of them walled gardens.

Currently if you’re a new site that needs the social graph (e.g. dopplr.com) to provide one fun & useful feature (e.g. where are your friends traveling and when?), then you face a much bigger problem then just implementing your main feature. You also have to have usernames, passwords (or hopefully you use OpenID instead), a way to invite friends, add/remove friends, and the list goes on. So generally you have to ask for email addresses too, requiring you to send out address verification emails, etc. Then lost username/password emails. etc, etc. If I had to declare the problem statement succinctly, it’d be: People are getting sick of registering and re-declaring their friends on every site., but also: Developing “Social Applications” is too much work.

Facebook’s answer seems to be that the world should just all be Facebook apps. While Facebook is an amazing platform and has some amazing technology, there’s a lot of hesitation in the developer / “Web 2.0″ community about being slaves to Facebook, dependent on their continued goodwill, availability, future owners, not changing the rules, etc. That hesitation I think is well-founded. A centralized “owner” of the social graph is bad for the Internet. I’m not saying anybody should ban Facebook, though! Far from it. It’s a great product, and I love it, but the graph needs to exist outside of Facebook. MySpace also has a lot of good data, but not all of it. Likewise LiveJournal, Digg, Twitter, Zooomr, Pownce, Friendster, Plaxo, the list goes on. More important is that any one of these sites shouldn’t own it; nobody/everybody should. It should just exist.

Goals:

  1. Ultimately make the social graph a community asset, utilizing the data from all the different sites, but not depending on any company or organization as “the” central graph owner.
    1. Establish a non-profit and open source software (with copyrights held by the non-profit) which collects, merges, and redistributes the graphs from all other social network sites into one global aggregated graph. This is then made available to other sites (or users) via both public APIs (for small/casual users) and downloadable data dumps, with an update stream / APIs, to get iterative updates to the graph (for larger users)

    2. While the non-profit’s servers and databases will initially be centralized, ensure that the design is such that others can run their own instances, sharing data with each other. Think ‘git‘, not ‘svn‘. Then whose APIs/servers you use is up to you, as a site owner. Or run your own instance.
  2. For developers who don’t want to do their own graph analysis from the raw data, the following high-level APIs should be provided:
    1. Node Equivalence, given a single node, say “brad on LiveJournal”, return all equivalent nodes: “brad” on LiveJournal, “bradfitz” on Vox, and 4caa1d6f6203d21705a00a7aca86203e82a9cf7a (my FOAF mbox_sha1sum). See the slides for more info.
    2. Edges out and in, by node. Find all outgoing edges (where edges are equivalence claims, equivalence truths, friends, recommendations, etc). Also find all incoming edges.
    3. Find all of a node’s aggregate friends from all equivalent nodes, expand all those friends’ equivalent nodes, and then filter on destination node type. This combines steps 1 and 2 and 1 in one call. For instance, Given ‘brad’ on LJ, return me all of Brad’s friends, from all of his equivalent nodes, if those [friend] nodes are either ‘mbox_sha1sum’ or ‘Twitter’ nodes.
    4. Find missing friends of a node. Given a node, expand all equivalent nodes, find aggregate friends, expand them, and then report any missing edges. This is the “let the user sync their social networking sites” API. It lets them know if they were friends with somebody on Friendster and they didn’t know they were both friends on MySpace, they might want to be.

    But more generally, for developers, enabling new kinds of apps we haven’t been able to think of yet.

  3. For end-users:
    1. A user should then be able to log into a social application (e.g. dopplr.com) for the first time, ideally but not necessarily with OpenID, and be presented with a dialog like,

      “Hey, we see from public information elsewhere that you already have 28 friends already using dopplr, shown below with rationale about why we’re recommending them (what usernames they are on other sites). Which do you want to be friends with here? Or click ’select-all’.”

      Also every so often while you’re using the site dopplr lets you know if friends that you’re friends with elsewhere start using the site and prompts you to be friends with them. All without either of you re-inviting/re-adding each other on dopplr… just because you two already declared your relationship publicly somewhere else. Note: some sites have started to do things like this, in ad-hoc hacky ways (entering your LJ username to get your other LJ friends from FOAF, or entering your email username/password to get your address book), but none in a beautiful, comprehensive way.

    2. Deliver end-user tools (likely a browser add-on) to let users manage their social networks (whether the sites have cooperative APIs or not), syncing them with each other, or doing whatever they’d like, but according to the user’s own policies. While the tools will most likely add the most value with uncooperative sites, it must always be clear to users what is happening so that no one is ever tricked. More on this later…
    3. Make graph data as portable as documents are on a personal computer. (though likely never using the word ‘graph’ to end-users)

Non-Goals:

  1. The goal is not to replace Facebook. In fact, most people I’ve talked to love Facebook, just want a bit more of their already-public data to be more easily accessible, and want to mitigate site owners’ fears about any single data/platform lock-in. Early talks with Facebook about participating in this project have been incredibly promising.
  2. The goal is not to build a social networking site or anything that’s fun for the end-user. Rather, the goal is to build the guts that allow a thousand new social applications to bloom, like Dopplr, etc. Do one thing and do it well. It will be most powerful to instead merge little isolated social graphs into one big social graph and spread it far and wide, for all to enjoy.
  3. The goal is not to replace Plaxo.
  4. The goal is not to replace __________.

Assumptions:

  1. The social graph contains a combination of public nodes, private nodes, public edges, and private edges. The focus is only on public data for now, as that’s all you can spray around the net freely to other parties. While focusing on public data doesn’t solve 100% of the problem, it does solve, say, 90% of the problem at 10% of the complexity. Private data can be added later, perhaps at a higher layer. For now, only public data.
  2. In addition, the focus is primarily on friend data, not data like photos (see movemydata.org), and not Date of Birth, Hometown, Interests, etc. There are plans on how to model a lot of that public non-content, non-friend profile data in the graph, and the plan is do that later, but that’s definitely Phase Two.
  3. There are both cooperative sites and uncooperative sites. Almost universally every small site I’ve talked to wants to cooperate, realizing their graphs are incomplete and that’s not their speciality… they just need the social graph to do their thing. They don’t care where it comes from and they don’t mind contributing their relatively small amount of data to making the global shared graph better. Uncooperative sites, on the other hand, are the ones that are already huge and either see value in their ownership of the graph or are just large enough to be apathetic on this topic. Please note that “uncooperative” doesn’t mean “actively fighting it”, but rather that they might just not prioritize supporting this. In any case, it must (and will) work with both types of sites over time.
  4. The world won’t switch en masse to anybody’s “social networking interop protocol”, pet XML format, etc. It simply won’t happen. This must all work supporting any and all ways of data collection, change notification, etc. Cute new protocols and XML/YAML/JSON formats for cooperative sites will help (and have already started to be deployed with a few early cooperative sites), but by and large, most sites won’t be cooperative at first, and some (e.g. MySpace) might not ever ever support this. This is going to happen one site at a time and without everybody speaking the same protocols. That said, this project will use open standards, microformats, etc in all data that is republished in, say, widgets (for those users who like widgets)
  5. Most users don’t care about XML, protocols, standards, data formats, centralization vs decentralization, silos, lock-in, etc. You, the reader of this document, are not a normal user. To reach the normal users, we must provide them value: some functionality, ease, bling, utility that they can’t get elsewhere. Good data begets users, and users begets good data. There are a bunch of ideas on how to bootstrap this cycle. More on that later, but fortunately a lot of the good data is already publicly accessible via good APIs and open data formats.
  6. Requiring browser add-ons or other end-user downloads is a nonstarter. This all must run primarily on the web. Some functionality for some (uncooperative) sites will require a browser plugin, but most won’t.
  7. While a browser add-on most likely will be used to facilitate friending/defriending and data acquisition on the user’s behalf for certain uncooperative sites, their browser must never be used (thus their IP address and user-agent string) to gather and report data that isn’t theirs. For instance, collecting their friends on a site like MySpace (if they configure it to) is okay, but scraping their friends-of-friends isn’t cool because that isn’t their data. It’s either those friends’ data or MySpace’s… definitely not the user who downloaded the add-on.
  8. It’s recognized that users don’t always want to auto-sync their social networks. People use different sites in different ways, and a “friend” on one site has a very different meaning of a “friend” on another. The goal is to just provide sites and users the raw data, and they can use it to implement whatever policies they want.

Development Status:

As of 2007-08-16, a lot of the above has already been prototyped:

  1. got the data to 5 large social networks, modeled them in the graph
  2. prototyped working implementations of the APIs above (lot of room for performance optimizations, caching, and parallelism, but wanted to get correctness first)
    1. Was able to find all my missing LiveJournal and Vox friends, based on my relationships elsewhere.
  3. start of a Firefox plug-in to work with MySpace
  4. start of a website to let users declare extra public nodes, node equivalences, and relationships that aren’t otherwise automatically picked up (website to include fun stats and widgets, as enticement for users to go there, as well as browser add-on downloads, to sync different sites, if they choose to do that)
  5. …

Future:

David Recordon has announced that he’s going to SixApart, largely to work on this sort of stuff. Plaxo is also doing interesting stuff in this regard. Eventually companies will build free and paid services atop this data, like trust/reputation APIs, which will help Movable Type & WordPress bloggers with identifying comment spam (once you have an OpenID-authenticated comment, you have a node, but then use the APIs to find out if that node is good).

In any case, a lot of people are working on this lately, and taking different approaches. It’s quite likely that multiple groups will converge to work on this together, similar to how many groups got together to work on OpenID.

How You Can Help:

You run a social networking site and have some node/edge (user/friend) data, or want to beta test some of the APIs? Get in touch… join the Google Group.

End-user who wants to try out the non-techy website and tools? You’re here early. :) Limited beta access for testers will be announced later, by whoever ends up building this.

Conclusion:

I’m excited about this. Start thinking about how you can take advantage of stuff like this. It’s going to be cool.

Related & Semi-Related Work

~ by innovationstartups on May 3, 2008.

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