Homeostasis in Social Networks

4 August, 2008

As a network grows and develops a power law distribution of connectivity, the resulting structure is heavily biased towards the initial nodes by virtue of their prior existence. Social networks therefore tend to be disproportionately dominated by the individuals who have been around the longest. This state is very stable, since information flow on the network is highly dependent on these hubs. However, the imbalance of attention may result in potentially valuable new members being neglected or moving elsewhere. Stagnation as a consequence of homeostasis might be one reason why one network gives way to another.

In living organisms a stable structure is desirable, and homeostatic mechanisms are present to maintain equilibrium. However, if an organism cannot break out of a given equilibrium state it may prove brittle and vulnerable to external pressures. The birth and death of individuals allows a tribe or species to adapt to a changing external environment. An alternative response to environmental changes is exhibited by cellular slime molds which, when food is scarce, merge into a “multicellular slug-like coordinated creature which crawls to an open lit place and grows into a fruiting body. Some of the amoebae become spores to begin the next generation, but some… sacrifice themselves to become a dead stalk, lifting the spores up into the air.”

Corporations and other large organisations also suffer from the effects of homeostasis. Although there is an entire industry devoted to the study of organisational structural dynamics and change management, stagnation is more often than not alleviated by market pressures, whether by acquisition or enforced “restructuring”. In our work lives we are each happy to accept a comfortable equilibrium state, but this reduces the ability of the organisation to adapt. And of course, when nation states are too rigid and authoritarian they tend to fall to revolution rather than evolution.

In the brain homeostasis might correspond to boredom resulting from a lack of stimulation. This reaction is perhaps intended to instigate a search for new ideas or experiences, which are generally rewarded by a feeling of pleasure. If something is new and exciting it’s usually fun too, because we enjoy learning. The desire for novelty provides a mechanism to move the mind out of an unhelpful state.

If a garden is left to nature, a power law distribution of species quickly develops. One or two particularly well-suited or vigorous plants take over whilst others dwindle. Gardeners address this by weeding and pruning. Even “wild gardens” require the careful application of a little encouragement and discouragement. When a new plant appears it must be nurtured whilst the weeds are kept in check.

If social sites like Digg, Wikipedia and Twitter are to remain dynamic and continually evolving they need to solve the homeostasis problem. In the social media, new and interesting contexts or individuals with novel viewpoints should somehow be amplified. There has been recent discussion on Twitter about how a modified Retweet could be useful, and perhaps this partly fulfills the need since the resulting amplification is relative to the connectivity of the sender, but it ultimately depends on the goodwill of community members. I have mentioned in previous posts how novelty can be identified with semantic profiling, but how can it be “subtly encouraged” without threatening the social ecosystem in question?


Weak Ties and Emergent Contexts in Semantic Networks

9 July, 2008

Addressing information overload by taking a complexity approach to semantics.

Toronto

Complexity Theory
The theory of complexity is based on the idea that complex behaviour emerges out of simple rules. Usually, we are talking about systems containing large numbers of interacting agents. In this sense ‘complex’ means that behaviour cannot be extrapolated from the rules of the system, only derived from models.

Living systems can be defined in terms of dissipative structures. These structures exist at the edge of chaos, or on the line between order and randomness. This means there is enough order for a distinct structure to be maintained over time, but enough randomness to allow for flexibility and evolution of that structure. This is usually achieved in living systems by gradually replacing the components of the system, by ingestion and excretion. For example, the human body is a continuous structure whose chemistry and components change over time. So is a human cell, and at a more basic level within the cell there are autocatalytic sets of enzymes and reactions which in the right circumstances are self-perpetuating.
 
Complex Networks
Complex networks can be found all over the natural and human world. Examples include the neocortex, the economy, ecosystems, control gene expression, metabolic pathways, contagion of disease or ideas, structure of the internet or languages etc. This is because they all are formed in a similar way following a few principles like growth by addition and preference for existing highly connected nodes. For example think of how the internet started, where people linked to a few popular sites, making them more popular, which made more people likely to link to them etc.

Small World Networks
This kind of growth often results in a scale-free network, whereby the distribution of node degree (how many links a node has) is described by a power law, or in other words follows a logarithmic graph. Another example of a power law is the ‘long tail’ curve utilised by iTunes, Amazon etc, who make a large proportion of their money from small volume sales of a large number of relatively unpopular items. For the same reason the size of an avalanche or earthquake is never predictable but fits statistically into a power law distribution.

Perhaps the best known example is the social network, which is also a small world network. The power law means that although most people only have a few contacts, there are a few social hubs with hundreds. Most people know about the ‘6 degrees’ idea in human society, where we are all connected to everyone else by around six steps in the network. A small world network achieves this by having hubs and weak ties, both of which emerge from the rules governing network growth.
 
Weak Ties
The point is that small world networks are very stable. If you take out a randomly chosen node then there are always plenty of other routes through the network. On the other hand, a strictly hierarchical network is vulnerable since removal of a single node will prevent information flow to all the nodes beneath it. This is also why some infectious diseases or computer viruses are never wiped out. There is a tipping point which occurs during formation of a network when the small world state is reached. After this, overall connectivity is not really improved by adding new connections. This state is achieved by weak ties, which are connections between nodes which don’t have overlapping neighbourhoods. For instance, I know people in Vancouver so friends in New Zealand are only two steps away from them in the social network.
 
Complex Network Dynamics
Why is this relevant to organisations? This is about how a network of humans responds to the outside world, which is a function of information flow and network growth. Or in other words, the dynamics IN the network and the dynamics OF the network. Open Space Technology is an example of how self-organisation can be encouraged within groups of people by flattening hierarchy, which helps create new weak ties between different levels of an organisation. This increases information flow through the network which strengthens it and enables it to be more adaptable.

Spider organisations, typically large hierarchical corporations, are slow, ordered and brittle. Starfish organisations, typically loose associations of individuals or smaller organisations, are more adaptable because their ability to move quickly allows them to change with the environment. Look at the impact of guerrilla tactics on warfare. Traditional armies have a hierarchical command structure, where information has to pass up the chain of command before decisions can be made centrally. They are slow to mobilise and suffer if key components are lost. The guerrilla approach is to have a distributed organisation of cells without a central command center, which can collaborate or act autonomously when required. They usually share general principles but are free to make decisions. Consider the account of Apache resistance in The Starfish and The Spider for an example of a successful leaderless organisation.
 
Semantic Profiling
Semantic profiling software reads text in order to represent context by extracting themes and the relationships between them. Context representations can be used for discovery, mapping, exploration etc. The topology of a semantic network is similar to a social network, so many of the same tools can be used. A semantic approach is complementary to manual tagging, partly due to the emphasis on implicit relationships rather than pre-defined categories, and partly because automation makes it consistent, which supports the pattern matching functions.

Whether a semantic or a manual approach is used, I believe that focusing on emergent relationships can help knowledge systems avoid the categorisation and domain evolution problems that top-down taxonomies suffer from. An example would be for expertise location or ad hoc team building in crisis/disaster management, or other applications where the emphasis is on immediate action.
 
Hierarchical Temporal Memory
It is currently maintained that the neocortex is a machine for matching patterns in time. Tracking the emergence of semantic relationships over time may be the only way we can realistically make sense of the huge amount of information most of us are bombarded with these days. Psychologists also theorise that a new concept may often come about by forming a relationship between two previously unrelated ideas.

I like to think that having a new idea is equivalent to forming a weak tie in the semantic network. The feeling of insight corresponds to linking previously unconnected ideas. As context then develops around the new relationship, nodes with no overlapping context gradually become more and more embedded. For instance, the words ‘mobile’ and ‘phone’ were once unrelated, but now have almost transitioned to a single word state due to their association. The key is to identify these new weak ties as they appear and start to develop surrounding context. For instance, ‘this article is related to those you have read but also contains some interesting new ideas’…