Robert Schiaffino

Examining the Thomas Paine Corpus: Automated Computer Authorship Attribution Methodology Applied to Thomas Paine’s Writings

Thomas Paine was one of the most widely read and influential writers in the era of Democratic Revolutions in the eighteenth and early nineteenth centuries. He was a philosopher and a political leader who affected the course of the great revolutions in America and France, and whose writings still have relevance in modern politics. It is important to clarify the body of work that Paine produced. There has been speculation as to what actually were his writings, what has been left out of collections, and what has been mistakenly added to his works. For example, Philip Foner in his once authoritative Complete Writings of Thomas Paine, includes “An Occasional Letter on the Female Sex” and remarks, “Although there is evidence to prove that this article … was not written by Paine … it has been included in the present edition” because it may represent the sentiments of Paine.1 “African Slavery in America” was left out of Eric Foner’s Thomas Paine: Collected Writings, indicating some doubt to the attribution to Paine.2 Until an accurate collected works can be established, the scholarship on Paine will remain incomplete, and the public and academia will continue to misattribute quotes and opinions to Paine without historical support. The goal of our text analysis project is to address this deficiency.

Bicliques in Graphs with Correlated Edges: From Artificial to Biological Networks

Networks representing complex biological interactions are often very intricate and rely on algorithmic tools for thorough quantitative analysis. In bi-layered graphs, identifying subgraphs of potential biological meaning relies on identifying bicliques between two sets of associated nodes, or variables – for example, diseases and genetic variants. Researchers have developed multiple approaches for forming bicliques and it is important to understand the features of these models and their applicability to real-life problems. We introduce a novel algorithm specifically designed for finding maximal bicliques in large datasets. In this study, we applied this algorithm to a variety of networks, including artificially generated networks as well as biological networks based on phenotype-genotype and phenotype-pathway interactions. We analyzed performance with respect to network features including density, node degree distribution, and correlation between nodes, with density being the major contributor to computational complexity. We also examined sample bicliques and postulate that these bicliques could be useful in elucidating the genetic and biological underpinnings of shared disease etiologies and in guiding hypothesis generation. Moving forward, we propose additional features, such as weighted edges between nodes, that could enhance our study of biological networks.