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
8/29/10
Weblog (5): Time and Space Dimensions
Time and space dimensions
The dimensions of space and time within digital communities differ greatly from traditional social structures. Within heavily networked societies, the value ‘time’ is used selectively, without the need for a specific space. New digital technologies facilitate a freedom from time, a cultural escape from the demands and pressures of traditional clock time (Castells, 2000).
In the technologically advanced civilisations of the first world, time and space have almost been nullified. The way people partake in digital communities, how they interact with, experience and conduct themselves within their spheres of living, are almost boundless with regards to those two dimensions. Castells (2000) refers to the use of technology to escape the dimension of time and labelled the social time of a network society as ‘timeless time’.
Ferguson (1990) argues that space and time cannot completely be nullified. Both are still vital to certain strategic decisions made. In particular, corporations, entrepreneurs and individuals remain attentive to location and schedules with respect to their business activities, especially those business activities that are influenced by local needs and decisions.
Time restrictions are therefore broken by the spread of customs or traditions while space restrictions are broken by the elevating reach of communication and distribution (Van Dijk, 2006). In each case information is stored to be used at the individual actor’s discretion or saved as a resource for anticipated future needs (2006).
Though space and time dimensions in the digital sphere are indeed diminishing, they currently appear to remain important within traditional spheres.
References:
Ferguson, M. 1990. ‘Electronic media and the redefining of time and space’, in M. Ferguson (ed.), Public Communication: the New Imperatives. London: Sage Publications Ltd.
Van Dijk, J. 2006. The Network Society: Social Aspects of New Media. 2nd ed. London: Sage Publications Ltd.
Castells, M. 2000. The rise of the network society. 2nd ed. Oxford: Blackwell Publishers
The dimensions of space and time within digital communities differ greatly from traditional social structures. Within heavily networked societies, the value ‘time’ is used selectively, without the need for a specific space. New digital technologies facilitate a freedom from time, a cultural escape from the demands and pressures of traditional clock time (Castells, 2000).
In the technologically advanced civilisations of the first world, time and space have almost been nullified. The way people partake in digital communities, how they interact with, experience and conduct themselves within their spheres of living, are almost boundless with regards to those two dimensions. Castells (2000) refers to the use of technology to escape the dimension of time and labelled the social time of a network society as ‘timeless time’.
Ferguson (1990) argues that space and time cannot completely be nullified. Both are still vital to certain strategic decisions made. In particular, corporations, entrepreneurs and individuals remain attentive to location and schedules with respect to their business activities, especially those business activities that are influenced by local needs and decisions.
Time restrictions are therefore broken by the spread of customs or traditions while space restrictions are broken by the elevating reach of communication and distribution (Van Dijk, 2006). In each case information is stored to be used at the individual actor’s discretion or saved as a resource for anticipated future needs (2006).
Though space and time dimensions in the digital sphere are indeed diminishing, they currently appear to remain important within traditional spheres.
References:
Ferguson, M. 1990. ‘Electronic media and the redefining of time and space’, in M. Ferguson (ed.), Public Communication: the New Imperatives. London: Sage Publications Ltd.
Van Dijk, J. 2006. The Network Society: Social Aspects of New Media. 2nd ed. London: Sage Publications Ltd.
Castells, M. 2000. The rise of the network society. 2nd ed. Oxford: Blackwell Publishers
Weblog (4); Small World Theory & Searchability
Small World Theory & Searchability
In a desire to improve an individual’s standard of living one needs to examine the social network in which they partake and interact. This data provides them with insight on the quality of the network.
Individuals generally express their preferences for one individual over the other (or one network over another). It is this preferential attachment that elevates the growth of well-connected networks above weaker ones (Granovetter, 1973, Watts, 1999, 2003, 2004). The outcome is a scale-free network rather than a small world network, or generalized affiliated network, as illustrated by the work of Krapivsky, et al (2000) and Barabasi & Albert (1999).
Individuals connect on the premise that they share a common social dimension. The dynamics of social networks attempting to increase the standard of living greatly depends on the reciprocal value it supports for each individual. The social dimensions of the network, the social class, demographics and so forth determine whether an individual searches and connects to the ‘new’ network. This relation reflects the model of generalized affiliation networks of Watts (Watts, 2002, 2003).
The predisposed thought that adding random connections to a network could potentially ruin the network has been refuted by Watts and Strogatz’s (Watts, 1998) model. The model found that if an individual added a few random connections into a complex network, the individual could make the network both more efficient and effective. In fact, randomness could dramatically improve the performance of a complex system rather than ruining it (CBS Interactive, 2010).
Watts (2004) argued that this social network model should be extended to enable search-ability based on the fact that short paths existed between randomly depicted individuals on a local basis. The model enabled the use of local information to search for others; however, the model did not support the search-ability feature when searching for people or organizations foreign to the network. It was initially Milgram, and thereafter Kleinberg (Kleinberg, 2000a), that discovered and added the capability of searches outside a specific network to the network model of Watts & Strogatz (Watts, 2004). In fact, now one could search for information on a global scale instead of primarily on a local one.
References:
Barabasi, A.-L., Albert, R. 1999. Emergence of scaling in random networks. Science, 286, 509-12.
Bianconi, G., Barabasi, A.L. 2001. Competition and multiscaling in evolving networks. EDP Sciences, 54, 436-442.
CBS Interactive, C. 2010. Network theory's new math [Online]. Available: http://news.cnet.com/2009-1069-978596.html [Accessed 27 August 2010].
Kleinberg, J. 2000a. The small-world phenomenon: an algorithmic perspective. Proc. 32nd ACM Symphony Theory Computing, 32, 163-170.
Krapivsky, P. L., Redner, S. & Leyvraz, F. 2000. Connectivity of growing random networks. Phys. Rev. Lett, 85, 4629-32.
Watts, D. J. 1999. Small Worlds: The Dynamics of Networks between Order and Randomness, Princeton, Princeton University Press.
Watts, D. J. 2001. A simple model of global cascades on random networks. PNAS, 99, 5766-5771.
Watts, D. J. 2002. A simple model of information cascades on random networks. Proc. Natl. Acad. Sci. USA, 99, 5766-71.
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.
Watts, D. J., Strogatz, S.H. 1998. Collective dynamics of 'small-world' networks. Nature, 393.
In a desire to improve an individual’s standard of living one needs to examine the social network in which they partake and interact. This data provides them with insight on the quality of the network.
Individuals generally express their preferences for one individual over the other (or one network over another). It is this preferential attachment that elevates the growth of well-connected networks above weaker ones (Granovetter, 1973, Watts, 1999, 2003, 2004). The outcome is a scale-free network rather than a small world network, or generalized affiliated network, as illustrated by the work of Krapivsky, et al (2000) and Barabasi & Albert (1999).
Individuals connect on the premise that they share a common social dimension. The dynamics of social networks attempting to increase the standard of living greatly depends on the reciprocal value it supports for each individual. The social dimensions of the network, the social class, demographics and so forth determine whether an individual searches and connects to the ‘new’ network. This relation reflects the model of generalized affiliation networks of Watts (Watts, 2002, 2003).
The predisposed thought that adding random connections to a network could potentially ruin the network has been refuted by Watts and Strogatz’s (Watts, 1998) model. The model found that if an individual added a few random connections into a complex network, the individual could make the network both more efficient and effective. In fact, randomness could dramatically improve the performance of a complex system rather than ruining it (CBS Interactive, 2010).
Watts (2004) argued that this social network model should be extended to enable search-ability based on the fact that short paths existed between randomly depicted individuals on a local basis. The model enabled the use of local information to search for others; however, the model did not support the search-ability feature when searching for people or organizations foreign to the network. It was initially Milgram, and thereafter Kleinberg (Kleinberg, 2000a), that discovered and added the capability of searches outside a specific network to the network model of Watts & Strogatz (Watts, 2004). In fact, now one could search for information on a global scale instead of primarily on a local one.
References:
Barabasi, A.-L., Albert, R. 1999. Emergence of scaling in random networks. Science, 286, 509-12.
Bianconi, G., Barabasi, A.L. 2001. Competition and multiscaling in evolving networks. EDP Sciences, 54, 436-442.
CBS Interactive, C. 2010. Network theory's new math [Online]. Available: http://news.cnet.com/2009-1069-978596.html [Accessed 27 August 2010].
Kleinberg, J. 2000a. The small-world phenomenon: an algorithmic perspective. Proc. 32nd ACM Symphony Theory Computing, 32, 163-170.
Krapivsky, P. L., Redner, S. & Leyvraz, F. 2000. Connectivity of growing random networks. Phys. Rev. Lett, 85, 4629-32.
Watts, D. J. 1999. Small Worlds: The Dynamics of Networks between Order and Randomness, Princeton, Princeton University Press.
Watts, D. J. 2001. A simple model of global cascades on random networks. PNAS, 99, 5766-5771.
Watts, D. J. 2002. A simple model of information cascades on random networks. Proc. Natl. Acad. Sci. USA, 99, 5766-71.
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.
Watts, D. J., Strogatz, S.H. 1998. Collective dynamics of 'small-world' networks. Nature, 393.
8/23/10
Weblog (3): The Small World Theory on the Online Landscape
The Small World Theory on the Online Landscape
Studies have found that a network of interrelated web pages complies to some extent with small world theory. Krapivsky, et al (2000) and Barabasi & Albert (1999) argue that this type of network is a scale-free network rather than a network that complies to the small world theory. More on this topic can be found on Kooren’s blog [scale-free networks].
Instead of referring to nodes as individuals, and links as various social interactions, for this type of network nodes are referred to as documents, and links as URLs (Bianconi & Barabasi, 2001). In this context, two documents or sites on the internet are separated by only a small number of mouse clicks (Johnson, 2000). A network of interrelated web pages possesses a high degree of order, Barabasi and Albert (1999) observed. Universally centralised around hubs, they represent the limited number of nodes that may be linked to other organized networks (Barabasi, 1999). As a result, while the network of the internet is quite stable as a whole, the individual connections between nodes are themselves susceptible to crashes. This is because centralised hubs are connected to these nodes, and when a few nodes are removed, the system can potentially fall apart (Johnson, 2000).
Scientists such as Watts (Socialontology, 2008) have applied the discoveries made about the network of the internet to the structures of social networks. They observed similarities between the interconnectedness made possible by the web in comparison with that of physical human relations (Barabasi, 1999, Newman, Barabasi & Watts, 2006, Watts, 2004). Watts further (Socialontology, 2008, 2008) stated that this could contribute to a breakthrough in how scientists might synthesise new information about the internet with the medical field. This could potentially provide scientists with a better understanding of how disease spreads through a human population.
References:
Barabasi, A. L., Albert, R. 1999. Emergence of scaling in random networks. Science, 286, 509-12.
Bianconi, G., Barabasi, A.L. 2001. Competition and multiscaling in evolving networks. EDP Sciences, 54, 436-442.
Johnson, G. (2000). First Cells, Then Species, Now the Web. The New York Times Company. New York: New York viewed from www.nytimes.com
Krapivsky, P. L., Redner, S. & Leyvraz, F. 2000. Connectivity of growing random networks. Phys. Rev. Lett, 85, 4629-32.
Newman, M., Barabasi, A.L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton, Princeton University Press.
Socialontology. 2008. A documentary on networks, social and otherwise_Part 2 [Online]. Socialontology. Available: http://www.youtube.com/watch?v=n1-nfySqf9M [Accessed 10 August 2010].
Socialontology. 2008. A documentary on networks, social and otherwise_Part 1 [Online]. Socialontology. Available: http://www.youtube.com/watch?v=RcCpEf6_Ofg [Accessed 10 August 2010].
Watts, D. J. 2004. The “New” Science of Networks. Annual Review Sociology, 30, 243-270.
Studies have found that a network of interrelated web pages complies to some extent with small world theory. Krapivsky, et al (2000) and Barabasi & Albert (1999) argue that this type of network is a scale-free network rather than a network that complies to the small world theory. More on this topic can be found on Kooren’s blog [scale-free networks].
Instead of referring to nodes as individuals, and links as various social interactions, for this type of network nodes are referred to as documents, and links as URLs (Bianconi & Barabasi, 2001). In this context, two documents or sites on the internet are separated by only a small number of mouse clicks (Johnson, 2000). A network of interrelated web pages possesses a high degree of order, Barabasi and Albert (1999) observed. Universally centralised around hubs, they represent the limited number of nodes that may be linked to other organized networks (Barabasi, 1999). As a result, while the network of the internet is quite stable as a whole, the individual connections between nodes are themselves susceptible to crashes. This is because centralised hubs are connected to these nodes, and when a few nodes are removed, the system can potentially fall apart (Johnson, 2000).
Scientists such as Watts (Socialontology, 2008) have applied the discoveries made about the network of the internet to the structures of social networks. They observed similarities between the interconnectedness made possible by the web in comparison with that of physical human relations (Barabasi, 1999, Newman, Barabasi & Watts, 2006, Watts, 2004). Watts further (Socialontology, 2008, 2008) stated that this could contribute to a breakthrough in how scientists might synthesise new information about the internet with the medical field. This could potentially provide scientists with a better understanding of how disease spreads through a human population.
References:
Barabasi, A. L., Albert, R. 1999. Emergence of scaling in random networks. Science, 286, 509-12.
Bianconi, G., Barabasi, A.L. 2001. Competition and multiscaling in evolving networks. EDP Sciences, 54, 436-442.
Johnson, G. (2000). First Cells, Then Species, Now the Web. The New York Times Company. New York: New York viewed from www.nytimes.com
Krapivsky, P. L., Redner, S. & Leyvraz, F. 2000. Connectivity of growing random networks. Phys. Rev. Lett, 85, 4629-32.
Newman, M., Barabasi, A.L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton, Princeton University Press.
Socialontology. 2008. A documentary on networks, social and otherwise_Part 2 [Online]. Socialontology. Available: http://www.youtube.com/watch?v=n1-nfySqf9M [Accessed 10 August 2010].
Socialontology. 2008. A documentary on networks, social and otherwise_Part 1 [Online]. Socialontology. Available: http://www.youtube.com/watch?v=RcCpEf6_Ofg [Accessed 10 August 2010].
Watts, D. J. 2004. The “New” Science of Networks. Annual Review Sociology, 30, 243-270.
8/16/10
Weblog (2): Six Degrees of Separation: Strong & Weak Ties
Six Degrees of Separation: Strong & Weak Ties
Relationships in a network are commonly referred to as ‘ties’. To understand the impact of these ties in a network, it is paramount to look at their structure, strength, and value within the network (Granovetter, 1973, 1982, Watts, 2003). Ties are categorised as strong and weak ones. Weak ties are ties in the second or lesser degree, and bridge the gap between other networks or actors more easily than strong ties (Granovetter, 1982, Newman, Barabasi & Watts, 2006, Watts, 2003). They are also better able to accelerate the distribution of new information, as well as the collection, annotation and re-contextualization of content (Maher, 2010).
Strong ties on the other hand are generally connections in the first degree, such as those between relatives (Granovetter, 1973, 1982, Watts, 2003). Strong ties keep information within these ‘bordered’ relationships and they do not necessarily enrich and extend information and knowledge (Maher, 2010) beyond their existing network. Comparatively, these ties are at a disadvantage to weak ones when it comes to gathering new information, increasing the size of their network, or diffusing an innovation.
The ties people make effect the form of the network, and the form of the network effects the ties people make (Newman, Barabasi & Watts, 2006). The concept of the Six Degrees of Separation embodies the notion that weak ties beneficially mediate between new networks and actors at large. The interconnectivity and acceleration of (intermediated) communication highlights the importance of weak ties, as a multiplicity of weak ties quickens the distribution of new information, as well as the collection, annotation and re-contextualization of content.
References:
Maher, M. L. Year. Motivation and Collective Intelligence: Design Lessons In: Collective Intelligence 14 April 2010 University of Sydney.
Newman, M., Barabasi, A.L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton, Princeton University Press.
Watts, D. J. 2003. Six Degrees: The Science of a Connected Age, London, Vintage Books.
Relationships in a network are commonly referred to as ‘ties’. To understand the impact of these ties in a network, it is paramount to look at their structure, strength, and value within the network (Granovetter, 1973, 1982, Watts, 2003). Ties are categorised as strong and weak ones. Weak ties are ties in the second or lesser degree, and bridge the gap between other networks or actors more easily than strong ties (Granovetter, 1982, Newman, Barabasi & Watts, 2006, Watts, 2003). They are also better able to accelerate the distribution of new information, as well as the collection, annotation and re-contextualization of content (Maher, 2010).
Strong ties on the other hand are generally connections in the first degree, such as those between relatives (Granovetter, 1973, 1982, Watts, 2003). Strong ties keep information within these ‘bordered’ relationships and they do not necessarily enrich and extend information and knowledge (Maher, 2010) beyond their existing network. Comparatively, these ties are at a disadvantage to weak ones when it comes to gathering new information, increasing the size of their network, or diffusing an innovation.
The ties people make effect the form of the network, and the form of the network effects the ties people make (Newman, Barabasi & Watts, 2006). The concept of the Six Degrees of Separation embodies the notion that weak ties beneficially mediate between new networks and actors at large. The interconnectivity and acceleration of (intermediated) communication highlights the importance of weak ties, as a multiplicity of weak ties quickens the distribution of new information, as well as the collection, annotation and re-contextualization of content.
References:
Maher, M. L. Year. Motivation and Collective Intelligence: Design Lessons In: Collective Intelligence 14 April 2010 University of Sydney.
Newman, M., Barabasi, A.L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton, Princeton University Press.
Watts, D. J. 2003. Six Degrees: The Science of a Connected Age, London, Vintage Books.
8/11/10
Weblog (1); Six Degrees of Separation: Three Examples of Social Network Sites
Six Degrees of Separation: Three Examples of Social Network Sites
The Six Degrees of Separation, also known as the “Small World Effect” (Newman, Barabasi & Watts, 2006), is a fascinating concept with a complex internal architecture.
The Small World Effect shows:
“the average number of acquaintances that individuals possess and the probability of two randomly selected members of a society being linked by a chain of acquaintances consisting of one or two intermediaries.” (Watts, 2004, p 12)
The starting point for the theoretical investigation of the Small World Effect was the study of random-biased nets (Watts, 2004). Although developed in the 50s and 60s in the work of Rapoport, the concept was popularised by Pool & Kochen’s work in the 60s (Watts, 2004).
Social network sites increase the social capital of individuals by increasing their ability to contact friends, relatives, and business associates regardless of geographical proximity (Wellman, 2001). www.linkedin.com for example allows individuals to connect to others at the first, second and third degree of separation. If individuals wish to extend their network, and gain access to social capital beyond the first degree, they must solicit their request through one of the connecting actors within the network. This further demonstrates the importance of using weak ties to enable individuals to extend their social network (Granovetter, 1973, Watts, 1999, 2003).
www.delicious.com provides a different model through the use of a communal ‘repository’. This enables individuals to store, annotate and redistribute bookmarks. Members extend their network by suggesting bookmarks to others, as well as adding links to other websites or other nodes’ linked sites. These connections primarily consist of weak ties. Subsequently, user’s social capital is extended by connecting with other actors outside their immediate network (Wellman, 2001).
www.facebook.com by contrast applies a slightly different method for increasing social capital when compared to the two previous examples. In this model, once an actor is connected with another, the website aggregates contact information from existing connections to propose new connections based on mutual friendships between the connectors. Social capital is thus extended through using prior connections both on- and offline.
References:
Granovetter, M. 1973. The Strenght of Weak Ties. American Journal of Sociology, 78
Granovetter, M. 1982. The strength of weak ties: A network theory revisited. In: MARSDEN, P. V., LIN, N. (ed.) Social Structure and Network Analysis. California: Sage Publications Ltd.
Newman, M., Barabasi, A.L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton, Princeton University Press.
Watts, D. J. 1999. Networks, Dynamics, and the Small-World Phenomenon. American Journal of Sociology, 105, 493-527.
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.
Wellman, B. 2001. Computer Networks As Social Networks. Science, 293, 2031-2034.
The Six Degrees of Separation, also known as the “Small World Effect” (Newman, Barabasi & Watts, 2006), is a fascinating concept with a complex internal architecture.
The Small World Effect shows:
“the average number of acquaintances that individuals possess and the probability of two randomly selected members of a society being linked by a chain of acquaintances consisting of one or two intermediaries.” (Watts, 2004, p 12)
The starting point for the theoretical investigation of the Small World Effect was the study of random-biased nets (Watts, 2004). Although developed in the 50s and 60s in the work of Rapoport, the concept was popularised by Pool & Kochen’s work in the 60s (Watts, 2004).
Social network sites increase the social capital of individuals by increasing their ability to contact friends, relatives, and business associates regardless of geographical proximity (Wellman, 2001). www.linkedin.com for example allows individuals to connect to others at the first, second and third degree of separation. If individuals wish to extend their network, and gain access to social capital beyond the first degree, they must solicit their request through one of the connecting actors within the network. This further demonstrates the importance of using weak ties to enable individuals to extend their social network (Granovetter, 1973, Watts, 1999, 2003).
www.delicious.com provides a different model through the use of a communal ‘repository’. This enables individuals to store, annotate and redistribute bookmarks. Members extend their network by suggesting bookmarks to others, as well as adding links to other websites or other nodes’ linked sites. These connections primarily consist of weak ties. Subsequently, user’s social capital is extended by connecting with other actors outside their immediate network (Wellman, 2001).
www.facebook.com by contrast applies a slightly different method for increasing social capital when compared to the two previous examples. In this model, once an actor is connected with another, the website aggregates contact information from existing connections to propose new connections based on mutual friendships between the connectors. Social capital is thus extended through using prior connections both on- and offline.
References:
Granovetter, M. 1973. The Strenght of Weak Ties. American Journal of Sociology, 78
Granovetter, M. 1982. The strength of weak ties: A network theory revisited. In: MARSDEN, P. V., LIN, N. (ed.) Social Structure and Network Analysis. California: Sage Publications Ltd.
Newman, M., Barabasi, A.L & Watts, D.J. 2006. The Structure and Dynamics of Networks, Princeton, Princeton University Press.
Watts, D. J. 1999. Networks, Dynamics, and the Small-World Phenomenon. American Journal of Sociology, 105, 493-527.
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.
Wellman, B. 2001. Computer Networks As Social Networks. Science, 293, 2031-2034.
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