We improve on previous recommender systems by taking advantage of the layered structure of software. We use a random-walk approach, 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 Kleinberg's work [10], we approximate the steady-state of an infinite random walk on a subset of a callgraph in order to rank the functions by their steady-state probabilities. Surprisingly, this purely structural approach works quite well. Our approach, like that of Robillard's "Suade" algorithm [15], and earlier data mining approaches [13] relies solely on the always available current state of the code, rather than other sources such as comments, documentation or revision information. Using the Apache API documentation as an oracle, we perform a quantitative evaluation of our method, finding that our algorithm dramatically improves upon Suade in this setting. We also find that the performance of traditional data mining approaches is complementary to ours; this leads naturally to an evidence-based combination of the two, which shows excellent performance on this task.

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 the network. RESULTS: In recent social network studies, exponential random graph models have been used extensively to model global social network structure as a function of their 'local features'. Starting from those studies, we describe the exponential random graph models and demonstrate their utility in modeling the architecture of biological networks as a function of the prominence of local features. We argue that the flexibility, in terms of the number of available local feature choices, and scalability, in terms of the network sizes, make this approach ideal for statistical modeling of biological networks. We illustrate the modeling on both genetic and metabolic networks and provide a novel way of classifying biological networks based on the prevalence of their local features.

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 network models, and features are identified as important if they are statistically "unusual," i.e. occur surprisingly often or seldom. Naturally, more representative random models result in better feature identification. Since biological networks exhibit a modular structure (mostly pertaining to their hierarchical functional organization), random network models need be modular similarly. In this work we consider the problem of generating random network models that incorporate network modularity. Theoretically, the problem is equivalent to generating random decompositions of a graph into a given number of connected components. Here we describe two methods we have developed to do that and illustrate their utility on pertinent systems biology problems of feature scaling