A Visual Exploration of Complex Networks

Complexity is everywhere. It’s a structural and organizational principle that reaches almost every field imaginable, from genetics and social networks to food webs and stock markets. Contemporary scientific and technological accomplishments—including mapping the human genome, decoding neural networks and opening up the ocean to exploration—have seen our ability to generate and acquire information outpace our ability to make sense of it. With a surfeit of facts and few ways to synthesize them, “meaningful information” quickly becomes an oxymoron.

As our cultural artifacts are increasingly measured in gigabytes and terabytes, organizing, sorting and displaying information in an efficient way is crucial to advancing knowledge. From the incredibly vast (the history of science) to the very small (protein complexes), science’s visual dialect renders it both more dynamic and more innovative.

Collected here are a few of the many intriguing, and often beautiful, images that illustrate how the whole is more than the sum of its parts.

—Manuel Lima is an interaction designer at R/GA Interactive and teaches information design at the Parsons School of Design. He has worked for the Museum of the Moving Image and with the Parsons Institute for Information Mapping in research projects for the National Geospatial-Intelligence Agency.

Featured here are five images that beautifully illustrate complexity. Click on the thumbnails to see larger versions of each image. For more visual complexity, pick up a copy of the September issue of Seed.

Protein Homology Graph


This network maps protein function by connecting proteins that share sequence similarity. Each of the 30,727 vertices represents a protein, and each of the 1,206,654 connections represents a similarity in amino acid sequence.

“Since proteins with more sequence similarity are more likely to have related function, the network is a reasonable map of protein function,” said designer Alex Adai. “Different areas of the network tend to emphasize different functional classifications.  As a result, one can infer a protein’s function by the coordinate of the protein in the network.”

Neuronal Network


Three cortical mouse neurons were imaged at a magnification of 1,000x. The resulting picture shows how neurons join to form a complex network. According to author Mark Miller, a typical cortical neuron receives 1,000 to 10,000 contacts from other neurons and contacts 100 to 1,000 additional neurons.

“Ultimately, a lot of my research is oriented toward how neurons connect to one another to form neural circuits, and how the properties of particular types of neurons interact with their particular connectivity to endow the circuit with some set of functional properties,” Miller said.

PhylloTrees: Phyllotactic Patterns for Tree Layout


Researchers at the University of Calgary use the phyllotactic patterns of nature to layout hierarchical data.

“These naturally occurring patterns provide a non-overlapping, optimal packing when the total number of nodes is not known a priori,” authors Petra Neumann, Sheelagh Carpendale, and Anand Agarawala write on their website.

The designers start with a base node, and each hierarchical level of data adds a new set of branches to the tree. The top illustration is an example of a perfectly balanced tree not taken from an actual data set. The bottom tree is unbalanced and is derived from real data.

Botanical Visualization of Huge Hierarchies


“When we observe botanical trees, we find that the leaves, branches and their arrangement can often easily be extracted, in spite of their very large numbers,” says author Huub van de Wetering. “What would happen if we try to visualize hierarchical data as botanical trees?”

Wetering’s group at Holland’s Eindhoven University of Technology mapped hierarchical data to three-dimensional trees with branches and leaves representing files and directories. Groups of leaves are shown as fruit.

The World of Music: SDP Layout of High Dimensional Data


Researchers used a set of ratings from the LAUNCHcast team at Yahoo! to map artists, connecting those who were rated similarly by a large number of users.

“In the central-right portion of the image is a ‘diffuse’ set of points with some strong connections,” creator David Gleich said. “This region represents ‘mainstream’ music and includes many popular artists.  Immediately adjacent is a set of points that represents ‘indie’ music.  Thus, ‘indie’ music is not as independent as some might like to think.”

Originally published July 23, 2006


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