05-11-2014 | Sunday Sermon

From Manuel Lima's superb "Visual Complexity: Mapping Patterns of Information," p. 84

Time is one of the hardest variables to map in any complex system. It is also one of the richest. If we consider a social network, we can quickly realize that a snapshot in time can only tell us a bit of information about that community. Alternatively, if time were to be properly measured and mapped, it would provide us with a comprehensive understanding of the social group's changing dynamics. Out of the existing panoply of social network analysis tools available, very few offer the ability to explore the network over time, investigate how it expands or shrinks, how relationships evolve, and how certain nodes become more or less prominent. This, of course, should change.

Networks are evolving systems, constantly mutating and adapting. As physicists Mark Newman, Albert-Laszlo Barabasi, and Duncan J. Watts explain, "Many networks are the product of dynamical processes that add or remove vertices and edges ... the ties people make affects the form of the network, and the form of the network affects the ties people make. Social network structure therefore evolves in a historically dependent manner, in which the role of the participants and the patterns of behavior they follow cannot be ignored." In some cases, the changes do not take weeks or months, but minutes or hours. And it is not only the network that adapts: whatever is being exchanged within the system also fluctuates over time (e.g., information, energy, water, a virus).

If we consider the vast hidden networks that sustain our biosphere, we can truly understand how critical the dimension of time really is. After all, it is the particularly dynamic nature of interconnecting ecosystems around the world thet poses one of the most difficult challenges to our enduring effort to understand the intricacies of our planet. Even something seemingly as stable as the human brain is continuously adding or removing synapses -- the connections between neurons -- in a process associated with cognitive learning. Not to the mention the internet, with its constant flux of information and vast landscape of servers, frequently adds or disconnects machines from the network. And time analysis does not only cover historical evolution: it equally applies to real-time dynamics and oscillations.

But mapping time in any network, as computer scientist Chaomei Chen recognizes, "is one of the toughest challenges for research in information technology ... it is technically challenging as well as conceptually complex." Due to the extremely demanding nature of charting the passage of time within a network, most scientists and designers feel apprehensive about incorporating this dimension in many of the their executions, which in part explains the lack of projects in this realm. There is no doubt that when we embrace time, the difficulty of the task at hand increases tenfold, but if visualization is to become the fundamental tool in network discovery, it needs to make this substantial jump. Most networked systems are affected by the natural progressions of time, and their depiction is never complete unless this critical dimension becomes part of the equation.

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