Archive for the ‘Networks’ Category
Collective Dynamics of Complex Systems Research Group Seminar Series.
February 22, 2012 – “How Networks Changed the “Scale” of Our World”
Hiroki Sayama (Bioengineering & Systems Science and Industrial Engineering, Binghamton University)
In networks, cooperation trumps collaboration. Collaboration happens around some kind of plan or structure, while cooperation presumes the freedom of individuals to join and participate. Cooperation is a driver of creativity. Stephen Downes commented here on the differences:
collaboration means ‘working together’. That’s why you see it in market economies. markets are based on quantity and mass.
cooperation means ’sharing’. That’s why you see it in networks. In networks, the nature of the connection is important; it is not simply about quantity and mass …
You and I are in a network – but we do not collaborate (we do not align ourselves to the same goal, subscribe to the same vision statement, etc), we *cooperate*
One of the main organizing principles in real-world social, information and technological networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. Due to unavailability of labeled ground-truth data it is practically impossible to evaluate and compare different models and notions of communities on a large scale.
In this paper we identify 6 large social, collaboration, and information networks where nodes explicitly state their community memberships. We define ground-truth communities by using these explicit memberships. We then empirically study how such ground-truth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the non-overlapping parts of communities, in contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves.
Existing models of network communities do not capture dense community overlaps. We present the Community-Affiliation Graph Model (AGM), a conceptual model of network community structure, which reliably captures the overall structure of networks as well as the overlapping nature of network communities.
In economic geography the notion of the network has come to play a critical role in a range of debates. Yet networks are rarely construed in an explicit fashion. They are, rather, assumed as some sort of more enduring social relations. This paper seeks to foreground these implicit assumptions – and their limitations – by tracing the selective engagement of economic geography with network approaches in economic sociology. The perception of networks in economic geography is mainly informed by the network governance approach that is founded on Mark Granovetter‘s notion of embeddedness. By embracing the network governance approach, economic geography bypassed the older tradition of the social network approach. Economic geography thus discarded not only the concerns for network position and structure but also more calculative and strategic perceptions of networks prevailing in Ron Burt’s work. Beyond these two dominant traditions, economic geography has, more recently, started to tinker with the post-structuralist metaphor of the rhizome of actor-network theory while it took no notice of Harrison White’s notions of publics and polymorphous network domains.
In complex environments, weak hierarchies and strong networks are the best organizing principle. One good example of complexity that we can try to fathom is nature itself. Networks thrive in nature.
We are just beginning to realize how we can use networks as our primary form of living and working. It has not been a clean progression from one organizing mode to the next but rather each new form built upon and changed the previous mode. The network form not as a modifier of previous forms, but a form in itself that can address issues that the three other forms could not address. This point is very important when it comes to things like implementing social business (a network mode) within corporations (institutional + market modes). Real network models (e.g. wirearchy) are new modes, not modifications of the old ones.
The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.
This paper seeks to further substantiate and appreciate the importance of West Churchman’s pragmatic philosophy, and to propose the development of what we call the participatory and rhizomatic systems approach. The aim of rhizomatics is to create a deterritoriazation of current social ﬁelds and to make sense of the creation of the rhizomatic networks and ethics for the marginalized group in practice. This paper takes the contributions of Gilles Deleuze and Felix Guattari’s notion of rhizome on ethical reasoning and incorporates them into a test. It examines how ethics for the marginalized group can assist in appreciating and developing ethical management of any systemic intervention. The paper looks into what ethics for the marginalized group is and how it is achieved in the context of rhizomatic networks.
Our ideas about creativity, and particularly the most important kind–what Richard Ogle calls “breakthrough creativity“–are governed by a long-standing and deep-seated myth: “the mind inside the head.” From ancient times, philosphers of mind have held that important ideas and insights come from the individual brains of geniuses with awesome rational powers, whose minds seem to function on a higher plane than those of normal folk. In recent years, however, as advances in cognitive science and network science have highlighted the importance of the external world, the social, cultural, and economic context in which ideas are generated, a classic paradigm shift has occurred. Mihaly Csikszentmihalyi has posited the idea of the “extended mind,” radically suggesting that the source of creativity lies not inside of our heads and brains, but outside them, in the connections between people and ideas. There has also been a concurrent, growing recognition of the role that imagination and intuition play in scientific breakthroughs, where in earlier times it was thought that superior rational thinking and logic were responsible for such advances. In The Mind Out There, Richard Ogle describes this paradigm shift and crystallizes its nature and implications for the first time. He argues that developments in the study of cognitive science, network science, and complexity, now allow us to see and understand how breakthrough ideas happen in a much clearer way, offering the beginnings of “a new science of ideas“. The key to this science resides in what the author calls “idea-spaces,” a set of nodes in a network of people (and their ideas) that cohere and take on a distinctive set of characteristics and dynamics leading to the generation of breakthrough ideas.
For interaction, the challenge is engagement. Widening the circle of involvement means expanding who gets to participate. It is about inviting and including relevant, new and different voices. The measure is built on the social graph: how many of your friends know each other?
The network design principles successful organizations follow are:
- shortening the distance between two randomly picked files/nodes/people.
- getting more people who you personally know to know each other.