In Web 2.0 - the ‘me’ becomes ‘we’.
Web 2.0 took center stage when Microsoft launched its Internet Explorer 7.0 at the end of 2005. Ever since online social networks have flourished (O'Reilly, 2005). Web 2.0 has enabled the individual to collaborate, contribute, and commune as a networked individual on the Internet. Individuals engulf in new virtual relationships and extend their network across borders (Foth, et.al, 2009).
Online groups enhance innovation, dissemination and mobilize others (weak ties) for civic engagement. Conversely, individuals or groups that are not reaching out to online intermediated tools are potentially limiting themselves. They limit their opportunities to benefit from the online network. It bears significance to note that online networks are not the same as social networks. Online networks took center stage between the 19th – 20th century. By contrast, social networks have been around since humankind.
Users of the online network increase exponentially with the amount of individuals joining it. Participants and contributors have changed over time. Some of these aspects are determined by:
1. the attractiveness of the network
2. the benefits, values and beliefs of the people in it
3. the positions of the people in it (Foth, et.al, 2009)
And all these contribute to the phenomenon that mobilize the collectives, as Cook (2010) noted. That being said, all social networks aim to attract the ‘me’ to convert to ‘we’ to commune and further expand the network, whether it is on- or offline.
References:
Foth, M. Gibbs, M. & Satchell, C. 2009. ‘From Social Butterfly to Urban Citizen’, HCSNet Workshop. QUT Creative Industries Precinct, Brisbane, Queensland.
O'Reilly, T. 2005. What is Web 2.0 [Online]. O'Reilly Network. Available: http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html [Accessed 11 September 2010].
Cook, J. 2010. Week 9: lecture at the University of Sydney, 22 September 2010, 2 hours
Watts, D. J. 2003. Six Degrees: The Science of a Connected Age, London, Vintage Books.
9/29/10
Weblog (9); Being a Social Butterfly Can Be A Good Thing
Being a Social Butterfly Can Be A Good Thing
The term “social butterfly” was mentioned during Network Society class on 22 September 2010. Like real butterflies, social butterflies tend to move from one group to another. The term suggests that those individuals maintain many weak ties. They engage in ‘superficial’ relationships and surround themselves with a diverse group of people.
As Granovetter (1973) and Rogers (2003) stated social butterflies are the force behind the diffusion of influence, innovation, and data. They are the innovators and early adopters that affect the decision-making process of whether or not a new product will flourish or flounder; this is paramount for the diffusion of innovations. Besides being the source, they are the primary resource for distributing the message to others in the flow of communication. They potentially act as change agents in the social system (Rogers, 1995).
Other elements such as time, intensity, attractiveness, and reciprocity can provide us further insights on the force of those social butterflies (Granovetter, 1973). What the impact of their behavior is on the network.
For the diffusion of innovations one needs the help of weak ties (social butterflies) to bridge the gap between adjacent clusters for the diffusion of innovation. Watts calls them the vulnerable nodes (Watts, 2001, Watts, 2003). They have a low threshold. Thus, they are the first one to change (Rogers, 1995). They are the carriers of information and strive in a network that is neither too loosely nor strongly connected. Rogers (2003) stated that weak ties show a low in communication proximity – they connect two nodes that do not share networks links with a common set of other nodes. And they are heterophilous, which is excellent for the diffusion of an innovation too.
Moreover, if we aim to design a new product, collect new thoughts or disseminate information, it is better not to focus on the strong ties, but rather on the ‘superficial’ social butterfly. Greater outcome will yield if we take the importance of those into consideration.
References:
Granovetter, M.S 1973. The Strength of Weak Ties. American Journal of Sociology, Volume 78 (6), 1360-1380
Rogers, E. 1995, Diffusion of Innovations. New York: The Free Press (Simon & Schuster Inc).
Rogers, E. M. 2003. Diffusion Networks. In: CROSS, R., PARKER, A. & SASSON, L. (ed.) Networks in the knowledge economy. Oxford and New York: Oxford University Press.
Watts, D. J. 2001. A simple model of global cascades on random networks. PNAS, 99, 5766-5771.
Watts, D. J. 2003. Six Degrees: The Science of a Connected Age, London, Vintage Books.
The term “social butterfly” was mentioned during Network Society class on 22 September 2010. Like real butterflies, social butterflies tend to move from one group to another. The term suggests that those individuals maintain many weak ties. They engage in ‘superficial’ relationships and surround themselves with a diverse group of people.
As Granovetter (1973) and Rogers (2003) stated social butterflies are the force behind the diffusion of influence, innovation, and data. They are the innovators and early adopters that affect the decision-making process of whether or not a new product will flourish or flounder; this is paramount for the diffusion of innovations. Besides being the source, they are the primary resource for distributing the message to others in the flow of communication. They potentially act as change agents in the social system (Rogers, 1995).
Other elements such as time, intensity, attractiveness, and reciprocity can provide us further insights on the force of those social butterflies (Granovetter, 1973). What the impact of their behavior is on the network.
For the diffusion of innovations one needs the help of weak ties (social butterflies) to bridge the gap between adjacent clusters for the diffusion of innovation. Watts calls them the vulnerable nodes (Watts, 2001, Watts, 2003). They have a low threshold. Thus, they are the first one to change (Rogers, 1995). They are the carriers of information and strive in a network that is neither too loosely nor strongly connected. Rogers (2003) stated that weak ties show a low in communication proximity – they connect two nodes that do not share networks links with a common set of other nodes. And they are heterophilous, which is excellent for the diffusion of an innovation too.
Moreover, if we aim to design a new product, collect new thoughts or disseminate information, it is better not to focus on the strong ties, but rather on the ‘superficial’ social butterfly. Greater outcome will yield if we take the importance of those into consideration.
References:
Granovetter, M.S 1973. The Strength of Weak Ties. American Journal of Sociology, Volume 78 (6), 1360-1380
Rogers, E. 1995, Diffusion of Innovations. New York: The Free Press (Simon & Schuster Inc).
Rogers, E. M. 2003. Diffusion Networks. In: CROSS, R., PARKER, A. & SASSON, L. (ed.) Networks in the knowledge economy. Oxford and New York: Oxford University Press.
Watts, D. J. 2001. A simple model of global cascades on random networks. PNAS, 99, 5766-5771.
Watts, D. J. 2003. Six Degrees: The Science of a Connected Age, London, Vintage Books.
9/19/10
Weblog (8): Affiliation Networks on www.taggalaxy.de
Affiliation Networks on www.taggalaxy.de
Tag galaxy is a website that is designed to aggregate images from Flickr that comply with the tag names individuals have entered as their search criteria. The system then displays an image of a globe orbited by smaller planets of all the keyword-tagged image categories containing the tag-search word.
The assumption of the website is that every ‘similar’ image is tagged with relevant descriptions. One is then able to consider the tags to be reliable and consistent. For instance, an image of Bondi Beach might be tagged with: ‘Bondi’, ‘Bondi Beach’, or ‘beach’. Also, the more tags that are associated with an image, the more likely it is that the image will be picked up. The individual who uses Taggalaxy can potentially become part of an affiliation network based on the attractiveness (popularity) of certain tags that images are associated with (Watts 2003).
Similar in nature to Amazon, once an individual enters a word into the system, the site recommends books with affiliated tags that the individual may also wish to consider. This affiliation network consists of other people that have similar preferences for similar books on the one hand and general books on the other (Watts, 2003, 2004). Prospectively, the individual may entertain the offer, and has the option to join the affiliation network. Hence, actors connect on the premise that they share a common social dimension (Watts, 2003, 2004).
At the time of writing, the chances that an individual will see his/her posted image on Flickr reflected on Taggalaxy is less than six intermediaries (six clicks or tag searches) apart: Six Degrees of Separation. However, as more images are added, the odds any one person will see its own image in a tag-search seems less likely. Validating this calculation is beyond the scope of this blogpost.
References:
Watts, D. J. 2003. Six Degrees: The Science of a Connected Age, London, Vintage Books.
Watts, D. J. 2004. The “New” Science of Networks. Annual Review Sociology, 30, 243-270.
Tag galaxy is a website that is designed to aggregate images from Flickr that comply with the tag names individuals have entered as their search criteria. The system then displays an image of a globe orbited by smaller planets of all the keyword-tagged image categories containing the tag-search word.
The assumption of the website is that every ‘similar’ image is tagged with relevant descriptions. One is then able to consider the tags to be reliable and consistent. For instance, an image of Bondi Beach might be tagged with: ‘Bondi’, ‘Bondi Beach’, or ‘beach’. Also, the more tags that are associated with an image, the more likely it is that the image will be picked up. The individual who uses Taggalaxy can potentially become part of an affiliation network based on the attractiveness (popularity) of certain tags that images are associated with (Watts 2003).
Similar in nature to Amazon, once an individual enters a word into the system, the site recommends books with affiliated tags that the individual may also wish to consider. This affiliation network consists of other people that have similar preferences for similar books on the one hand and general books on the other (Watts, 2003, 2004). Prospectively, the individual may entertain the offer, and has the option to join the affiliation network. Hence, actors connect on the premise that they share a common social dimension (Watts, 2003, 2004).
At the time of writing, the chances that an individual will see his/her posted image on Flickr reflected on Taggalaxy is less than six intermediaries (six clicks or tag searches) apart: Six Degrees of Separation. However, as more images are added, the odds any one person will see its own image in a tag-search seems less likely. Validating this calculation is beyond the scope of this blogpost.
References:
Watts, D. J. 2003. Six Degrees: The Science of a Connected Age, London, Vintage Books.
Watts, D. J. 2004. The “New” Science of Networks. Annual Review Sociology, 30, 243-270.
9/1/10
Weblog (7); Scale-free Networks
Scale-free networks
In examining Small World Networks, Barabasi and Albert (1999) found that the degree distribution of these networks had no slope, also known as cutoff value. Barabasi and Albert investigated these networks in conjunction with the ‘trending’ of the power law distribution. In this context, when there is no cutoff value a scale-free distribution occurs, Watts (2004) observed. For this reason, Barabasi and Albert (1999) coined this manifestation as a scale-free network.
This discovery advanced and changed the thoughts about the “Small World Effect” (Newman, Barabasi & Watts, 2006). That being said, the Small World Effect has mostly been applied to the network structure of actors, link structure of the WWW, and Power Grid in Western USA (Watts. 2004). Addition to this the power law distribution (tail) did not occur in the Power Grid Analysis.
In real-world networks the links between others were right-skewed with a ‘heavy tail’, which illustrates that only a small amount of the hubs were many times better connected than the average of the majority of the nodes (Barabasi & Albert. 1999).
Barabasi appended two mechanisms to the Small World Model. These are population growth and preferential attachment. Simon (1955) and Price (1980) were the first ones accountable for these attributions. Their research shows that a scale-free network grows over time as new people join the network. However, they have a preference to join already well-connected people in the network – it is all what motivates us to join. This then contributes to a scale-free network.
Moving forward with what we experience on the Internet and in today’s business, an increasing number of businesses (referring to their physical location) are disappearing. They have lost the competition with the Internet.
A good example is Barnes & Nobles bookstores. What we experience in bookstores is what we label as ‘scarcity’. This potentially distorts our economy; scarcity in inventory, people, distribution, and so on. By contrast, scarcity does not (or limited) exist on the Internet. There is unlimited distribution, inventory, and so on. The Internet has become an attractive network, which is timeless, 24/7 available, and caters to a divers audience. All these aspects are no longer pertaining to physical location. Among others, it is the attractiveness of the online network that has people join.
Concluding the Internet is a scale-free network. It strives on the notion of its attractiveness and features such as timeless and borderless accessibility; rather than speaking about individuals as nodes the Internet’s nodes are documents and links are URLs (Bianconi, 2001) Networks are not static. They continuously increase and delete nodes and vertices, and they grow on the basis of preferential linking (2001).
References:
Barabasi, A.L., & Albert, R. 1999. Emergence of scaling in random networks. Science vol. 286 pp. 509-12
Bianconi, G. & Barabasi, Al. 2001. Competition and multiscaling in evolving networks. EDP Sciences, vol. 54 pp. 436-442.
Watts, DJ. 2004. The “New” Science of Networks. Annual Review Sociology. Vol. 30, pp. 243-270
Newman, M., Barabasi, A-L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton University Press, Princeton
In examining Small World Networks, Barabasi and Albert (1999) found that the degree distribution of these networks had no slope, also known as cutoff value. Barabasi and Albert investigated these networks in conjunction with the ‘trending’ of the power law distribution. In this context, when there is no cutoff value a scale-free distribution occurs, Watts (2004) observed. For this reason, Barabasi and Albert (1999) coined this manifestation as a scale-free network.
This discovery advanced and changed the thoughts about the “Small World Effect” (Newman, Barabasi & Watts, 2006). That being said, the Small World Effect has mostly been applied to the network structure of actors, link structure of the WWW, and Power Grid in Western USA (Watts. 2004). Addition to this the power law distribution (tail) did not occur in the Power Grid Analysis.
In real-world networks the links between others were right-skewed with a ‘heavy tail’, which illustrates that only a small amount of the hubs were many times better connected than the average of the majority of the nodes (Barabasi & Albert. 1999).
Barabasi appended two mechanisms to the Small World Model. These are population growth and preferential attachment. Simon (1955) and Price (1980) were the first ones accountable for these attributions. Their research shows that a scale-free network grows over time as new people join the network. However, they have a preference to join already well-connected people in the network – it is all what motivates us to join. This then contributes to a scale-free network.
Moving forward with what we experience on the Internet and in today’s business, an increasing number of businesses (referring to their physical location) are disappearing. They have lost the competition with the Internet.
A good example is Barnes & Nobles bookstores. What we experience in bookstores is what we label as ‘scarcity’. This potentially distorts our economy; scarcity in inventory, people, distribution, and so on. By contrast, scarcity does not (or limited) exist on the Internet. There is unlimited distribution, inventory, and so on. The Internet has become an attractive network, which is timeless, 24/7 available, and caters to a divers audience. All these aspects are no longer pertaining to physical location. Among others, it is the attractiveness of the online network that has people join.
Concluding the Internet is a scale-free network. It strives on the notion of its attractiveness and features such as timeless and borderless accessibility; rather than speaking about individuals as nodes the Internet’s nodes are documents and links are URLs (Bianconi, 2001) Networks are not static. They continuously increase and delete nodes and vertices, and they grow on the basis of preferential linking (2001).
References:
Barabasi, A.L., & Albert, R. 1999. Emergence of scaling in random networks. Science vol. 286 pp. 509-12
Bianconi, G. & Barabasi, Al. 2001. Competition and multiscaling in evolving networks. EDP Sciences, vol. 54 pp. 436-442.
Watts, DJ. 2004. The “New” Science of Networks. Annual Review Sociology. Vol. 30, pp. 243-270
Newman, M., Barabasi, A-L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton University Press, Princeton
Weblog (6): Social Dynamics of “Small World Theory”
On blog post three Network Society - Kim's Contributions I voiced the question: “why do we still fail in eliminating diseases, if we are able to trace these diseases commencing at the base (the hub)?”
Findings of Watts & Strogatz and Kretschmar & Morris with the Small World Theory in mind show that it is not that straightforward to answer this question.
The network model of disease spreading found by Kretschmar & Morris (1996) and Watts & Strogatz (1998) differs slightly from what has been found by other researchers. Kretschmar & Morris’s model illustrates the dynamics as an apparent function of the structure compared to the latter model. By contrast, Watts & Strogatz’s model only synthesizes the network structure that influence the acceleration and extent of disease transmission.
Kretschmar & Morris and Watts & Strogatz use the small world theory to analyze changes in the spreading dynamics, which are due to more structural characteristics rather than based on connectivity. They use connectedness as a fixed parameter in their analysis. Conversely, other researchers based their analysis on disconnected parameters, e.g., graphs and, fix the average number of connections per actor per graph or occurrence.
Watts & Strogatz envision that others will build upon their method of analysis in an attempt to advance the knowledge, but rather to be able to answer the above-mentioned question.
References:
Watts, D.J. & Strogatz, S.H. 1998. Collective dynamics of 'small-world' networks
Nature Vol. 393, pp. 440-442.
Kretschmar, M. & Morris, M. 1996. Measures of concurrency in networks and the spread of infectious disease. Math. Bioscience. Vol. 133, pp. 165–195
Findings of Watts & Strogatz and Kretschmar & Morris with the Small World Theory in mind show that it is not that straightforward to answer this question.
The network model of disease spreading found by Kretschmar & Morris (1996) and Watts & Strogatz (1998) differs slightly from what has been found by other researchers. Kretschmar & Morris’s model illustrates the dynamics as an apparent function of the structure compared to the latter model. By contrast, Watts & Strogatz’s model only synthesizes the network structure that influence the acceleration and extent of disease transmission.
Kretschmar & Morris and Watts & Strogatz use the small world theory to analyze changes in the spreading dynamics, which are due to more structural characteristics rather than based on connectivity. They use connectedness as a fixed parameter in their analysis. Conversely, other researchers based their analysis on disconnected parameters, e.g., graphs and, fix the average number of connections per actor per graph or occurrence.
Watts & Strogatz envision that others will build upon their method of analysis in an attempt to advance the knowledge, but rather to be able to answer the above-mentioned question.
References:
Watts, D.J. & Strogatz, S.H. 1998. Collective dynamics of 'small-world' networks
Nature Vol. 393, pp. 440-442.
Kretschmar, M. & Morris, M. 1996. Measures of concurrency in networks and the spread of infectious disease. Math. Bioscience. Vol. 133, pp. 165–195
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