1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result Automatic generation of graph models for complex networks by genetic programming 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper Alexander Bailey bailey.alex@gmail.com 6731 O'Neil St. Niagara Falls, Ontario L2J 1N3 Canada Mario Ventresca mario.ventresca@utoronto.ca 628 Fleet St, #1114 Toronto, Ontario M5V 1A8 Canada Beatrice Ombuki-Berman bombuki@brocku.ca 483 Simcoe Street Niagara-On-The-Lake, Ontario L0S 1J0 Canada 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition) Alexander Bailey 4. the abstract of the paper(s) Complex networks have attracted a large amount of research attention, especially over the past decade, due to their prevalence and importance in our daily lives. Numerous human-designed models have been proposed that aim to capture and model different network structures, for the purpose of improving our understanding the real-life phenomena and its dynamics in different situations. Ground breaking work in genetics, medicine, epidemiology, neuroscience, telecommunications, social science and drug discovery, to name some examples, have directly resulted. Because the graph models are human made (a very time consuming process) using a small subset of example graphs, they often exhibit inaccuracies when used to model similar structures. This paper represents the first exploration into the use of genetic programming for automating the discovery and algorithm design of graph models, representing a totally new approach with great interdisciplinary application potential. We present exciting initial results that show the potential of GP to replicate existing complex network algorithms. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies B, F 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission) For more than 50 years researchers have been designing and creating graph model algorithms by hand, designed to model specific phenomena observed in real networks. In 1998 Watts and Strogatz published their "small-world" model in Nature, and showed they were able to model the properties of short path lengths and large numbers of triangles which were prevalent in many real networks, and which could not be explained by random connectivity alone. One year later, the Brabási-Albert model was published in Science and offered a mechanism for creating degree distributions which followed a power law, an apparent feature in many real networks and a feature for which the Watts-Strogatz model offered no explanation. Both of these models have continued to be enormously useful in the field of network science, and have helped researchers to diagnose neurological disorders, to better understand natural languages, study gene duplication and metabolic processes to name only a few. However, we continue to strive to understand new and increasingly more complicated networks, such as the brain (see the human connectome project) and innovative new tools are required to develop models for these large and mysterious networks. The proposed GP system is able to accurately recreate the Random Graph model, the Small-world model and the Barabási-Albert models, which were originally published in the very top science journals and inspire an enormous amount of current research; and thus satisfies the following two of the eight criteria: (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. 7. a full citation of the paper (that is, author names; publication date; name of journal, conference, technical report, thesis, book, or book chapter; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Alexander Bailey, Mario Ventresca, and Beatrice Ombuki-Berman. 2012. Automatic generation of graph models for complex networks by genetic programming. In Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference (GECCO '12), Terence Soule (Ed.). ACM, New York, NY, USA, 711-718. 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Any prize money is to be divided equally among the co-authors. 9. a statement stating why the judges should consider the entry as "best" in comparison to other entries that may also be "human-competitive." The proposed GP system is very novel and able to recreate complex network algorithms heralded as breakthroughs when first proposed. Manually discovering these algorithms required time on the scales of months and years, whereas we can replicate their results within hours. Moreover, the original network models (Barabasi-Albert, Watts-Strogatz,etc), while tunable, are still restricted to generating graphs defined by their model formulations, limiting their ability to accurately model real-world phenomena. Our system does not suffer from such strong limitations and is therefore also more robust. The obvious impact of such a tool to a wide audience is thus the main aspect judges should keep in mind: that our tool can be used by those interested in understanding diverse problems such as genetics and drug discovery to social interaction and pandemic disease spread.