Zachary M. Saul

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MOTIVATION The functioning of biological networks depends in large part on their complex underlying structure. When studying their systemic nature many modeling approaches focus on identifying simple, but prominent, structural components, as such components are easier to understand, and, once identified, can be used as building blocks to succinctly describe(More)
We improve on previous <i>recommender systems</i> by taking advantage of the layered structure of software. We use a <i>random-walk approach</i>, mimicking the more focused behavior of a developer, who browses the caller-callee links in the callgraph of a large program, seeking routines that are likely to be related to a function of interest. Inspired by(More)
– Motivated by widely observed examples in nature, society and software, where groups of related nodes arrive together and attach to existing networks, we consider network growth via sequential attachment of linked node groups or graphlets. We analyze the simplest case, attachment of the three node-graphlet, where, with probability α, we attach a peripheral(More)
Our modern infrastructure relies increasingly on computation and computers. Accompanying this is a rise in the prevalence and complexity of computer programs. Current software systems (composed of an interacting collection of programs, functions, classes, etc.) implement a tremendous range of func-tionality, from simple mathematical operations to intricate(More)
— Biological networks are formalized summaries of our knowledge about interactions among biological system components , like genes, proteins, or metabolites. From their global topology and organization one can learn nontrivial, systemic properties of organisms. In studies of biological network organization empirical networks are typically compared to random(More)
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