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
8/29/10
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.
8/4/10
WK 1 _ Task Reasoning Unit
My specific interest in the Master of Digital Communication & Culture program inclusive of the course Network Societies stretches from its hands-on learning approach to its theoretical understanding. Prevalent in my prior and current professional roles, where Information Technology and its interaction, marketing and online media co-exist in social life and workplaces could benefit from an improved cross-functional, interdisciplinary understanding of network societies supported by its historical make-up.
I contemplate that this subject provides an excellent balance between theoretical and practical skills, and focuses on the nature of interaction within societies where I, as academic and professional will continue to interact and advance. My professional background clusters around the themes of emerging media, publishing, new business set-up, international proprietary information transfer and creative excellence; I am eager to advance my academic knowledge in order to address these practical experiences. My experience in industries – cross-functional – and within interdisciplinary roles shaped me where I’m at right now; I strongly believe that mastering this course will enable me to see the bigger picture within the use of technology and its impact on societies at large. As a seasoned entrepreneur, I am still dazzled by the way technology has changed the manner in which cultures think, speak, interact and ultimately alters our social capabilities. I am an advocate for applied knowledge and research above new knowledge and innovations of which I found that the latter hardly applies due to our ability of inventing something entirely new.
In last week’s lecture we briefly discussed the interrelationships between actors, technology, eco-system, and so forth. I envision that this subject will discuss and exemplify all appropriate interconnected disciplines that pay tribute to the future of network societies.
Therefore, my wish to partake in the subject comes from my strong belief that I will master and ultimately advance the knowledge, and apply this to my current and future professional roles; further strengthen my theoretical knowledge and skills, and provide the tools to prosper at top level management, in which I will shape and reason the way social and professional cultures interact with Digital Technology.
I contemplate that this subject provides an excellent balance between theoretical and practical skills, and focuses on the nature of interaction within societies where I, as academic and professional will continue to interact and advance. My professional background clusters around the themes of emerging media, publishing, new business set-up, international proprietary information transfer and creative excellence; I am eager to advance my academic knowledge in order to address these practical experiences. My experience in industries – cross-functional – and within interdisciplinary roles shaped me where I’m at right now; I strongly believe that mastering this course will enable me to see the bigger picture within the use of technology and its impact on societies at large. As a seasoned entrepreneur, I am still dazzled by the way technology has changed the manner in which cultures think, speak, interact and ultimately alters our social capabilities. I am an advocate for applied knowledge and research above new knowledge and innovations of which I found that the latter hardly applies due to our ability of inventing something entirely new.
In last week’s lecture we briefly discussed the interrelationships between actors, technology, eco-system, and so forth. I envision that this subject will discuss and exemplify all appropriate interconnected disciplines that pay tribute to the future of network societies.
Therefore, my wish to partake in the subject comes from my strong belief that I will master and ultimately advance the knowledge, and apply this to my current and future professional roles; further strengthen my theoretical knowledge and skills, and provide the tools to prosper at top level management, in which I will shape and reason the way social and professional cultures interact with Digital Technology.
8/2/10
WK 2 - Readings Q & A
Is there a transitional taking place from mass society to information society or network society?
Yes, there is certainly a transitional taking place, in which mass society cultivate one-way media and centralized identities, consequently information and network societies harvest multi-directional media and decentralized organizations. Mass society evolves around communities – shared identities – while, network societies are empowered by individuals – those who are linked by networks. The first one extends its wealth through strong ties with others, and the latter one through weak ties; these aspects are inherently linked to the impact of communication, of which the mass society through its strong ties uses face-to-face communication in comparison with the information and network society that use intermediated communication more often. However, it is strongly believed that both cannot be separated and are combined extensively (van Dijk. 2006). This is not limited to only communication, the types of organization are becoming increasingly intertwined – extend beyond horizontal coordination and vertical control of activities – as well.
Network and its characteristics
Van Dijk (2006) defines networks as a collection of links between elements of a unit. Units are often called systems, and a single link of two elements is called a relationship; networks are a mode of organization of complex systems in nature and society.
Castells (2004) argues that a network has no center. I contest this argument within the discipline of network marketing a.k.a. multi-channel or -level marketing, in which the center [of the pyramid/scheme] is represented by an organization or identity that initiated the scheme, and thus enables the flow of data and products to new actors joining the scheme; its structure is hierarchical; top-down; and, there are numerous ways to employ these schemes. In this assignment I shall not discuss the schemes’ sustainability.
Further, at least six levels of networks of which the most important ones – in relation to the course Network Societies – that can be identified, are social, technical and media networks. Networks characterize the relation of elements, and provide order to those who partake in the system. Networks are complicated ways of organizing matter and living systems. The type of network is determined by its history, players, organizations, relationships and interaction within its societies at large; based on these determinants, each network has differentiated characteristics, but all networks support a basic level of involvement (individual); they could gradually enhance by moving up some levels (group, societal, global) which inherently incorporate new characteristics such as collectivities and bureaucracies that pertain to each level.
Other network characteristics are:
• their cooperation and competition between networks
• their infinitive distance
• their survivability
• their self-reconfigurability
• their constitution of patterns of life
• their flexibility and scalability
How are networked modes of sociality different from other models?
The networked modes of sociality portrayed the cultural social movements of the 60s and 70s and illustrated the culture of personal freedom and social autonomy (Castells. 2004) of which capitalism and statism find themselves on the other end of the spectrum.
The main differences were that they were interlinked with civil rights movements, and marked an era of cultural diversity and the affirmation of minorities.
What are the advantages of “non-hierarchical networks over vertical-hierarchical systems?”
In a non-hierarchical network the basic levels are only partially connected in the higher levels. The pertaining units contain relations and structures that overlap with those at higher level. The main advantage of such structure is that it allows a more flexible organization and interaction between the ‘actors’ in the system (Kontopoulos, 1993), and the empowerment of specific levels is not determined by the ‘height’ of the level; there’s less autonomy and more multidirectionality and a continuous flow of interactive information processing (Castells. 2004).
On the contrary, vertical-hierarchical systems are just that what non-hierarchical networks are not. Basic levels are fully integrated in the higher levels, and thus higher levels supersede the previous ones, which feeds the empowerment of the higher levels above the lower ones. The system is more rigid, and as such does not necessarily cultivate smooth transitions.
By answering these questions, I have acknowledged that I have provided a more descriptive than critical synopsis apart from my experience in marketing.
Yes, there is certainly a transitional taking place, in which mass society cultivate one-way media and centralized identities, consequently information and network societies harvest multi-directional media and decentralized organizations. Mass society evolves around communities – shared identities – while, network societies are empowered by individuals – those who are linked by networks. The first one extends its wealth through strong ties with others, and the latter one through weak ties; these aspects are inherently linked to the impact of communication, of which the mass society through its strong ties uses face-to-face communication in comparison with the information and network society that use intermediated communication more often. However, it is strongly believed that both cannot be separated and are combined extensively (van Dijk. 2006). This is not limited to only communication, the types of organization are becoming increasingly intertwined – extend beyond horizontal coordination and vertical control of activities – as well.
Network and its characteristics
Van Dijk (2006) defines networks as a collection of links between elements of a unit. Units are often called systems, and a single link of two elements is called a relationship; networks are a mode of organization of complex systems in nature and society.
Castells (2004) argues that a network has no center. I contest this argument within the discipline of network marketing a.k.a. multi-channel or -level marketing, in which the center [of the pyramid/scheme] is represented by an organization or identity that initiated the scheme, and thus enables the flow of data and products to new actors joining the scheme; its structure is hierarchical; top-down; and, there are numerous ways to employ these schemes. In this assignment I shall not discuss the schemes’ sustainability.
Further, at least six levels of networks of which the most important ones – in relation to the course Network Societies – that can be identified, are social, technical and media networks. Networks characterize the relation of elements, and provide order to those who partake in the system. Networks are complicated ways of organizing matter and living systems. The type of network is determined by its history, players, organizations, relationships and interaction within its societies at large; based on these determinants, each network has differentiated characteristics, but all networks support a basic level of involvement (individual); they could gradually enhance by moving up some levels (group, societal, global) which inherently incorporate new characteristics such as collectivities and bureaucracies that pertain to each level.
Other network characteristics are:
• their cooperation and competition between networks
• their infinitive distance
• their survivability
• their self-reconfigurability
• their constitution of patterns of life
• their flexibility and scalability
How are networked modes of sociality different from other models?
The networked modes of sociality portrayed the cultural social movements of the 60s and 70s and illustrated the culture of personal freedom and social autonomy (Castells. 2004) of which capitalism and statism find themselves on the other end of the spectrum.
The main differences were that they were interlinked with civil rights movements, and marked an era of cultural diversity and the affirmation of minorities.
What are the advantages of “non-hierarchical networks over vertical-hierarchical systems?”
In a non-hierarchical network the basic levels are only partially connected in the higher levels. The pertaining units contain relations and structures that overlap with those at higher level. The main advantage of such structure is that it allows a more flexible organization and interaction between the ‘actors’ in the system (Kontopoulos, 1993), and the empowerment of specific levels is not determined by the ‘height’ of the level; there’s less autonomy and more multidirectionality and a continuous flow of interactive information processing (Castells. 2004).
On the contrary, vertical-hierarchical systems are just that what non-hierarchical networks are not. Basic levels are fully integrated in the higher levels, and thus higher levels supersede the previous ones, which feeds the empowerment of the higher levels above the lower ones. The system is more rigid, and as such does not necessarily cultivate smooth transitions.
By answering these questions, I have acknowledged that I have provided a more descriptive than critical synopsis apart from my experience in marketing.
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