Extracting information from large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities within the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations.

© 2007 Zhao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

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This is an Open Access article distributed under the terms of the Creative Commons Attribution License

In the last few years, large-scale experiments have generated genome-wide protein in-teraction networks for many organisms including Saccharomyces cerevisiae (baker’s yeast), Caenorhabditis elegans (worm) and Drosophila melanogaster (fruit fly). In this thesis, we examine the vertex and edge betweenness centrality measures of these graphs. These measures capture how “central ” a vertex or an edge is in the graph by considering the fraction of shortest paths that pass through that vertex or edge. Our primary observation is that the distribution of the vertex betweenness centrality follows a power law, but the distribution of the edge betweenness centrality has a “Poisson-like ” distribution with a very sharp spike. To investigate this phenomenon, we generated random networks with degree distribution identical to those of the pro-tein interaction networks. To our surprise, we found out that the random networks and the protein interaction networks had almost identical distribution of edge be-tweenness. We conjecture that the “Poisson-like ” distribution of the edge betweenness centrality is the property of any graph whose degree distribution satisfies power law. Acknowledgments

of biological network centralities with CentiBiN

Abstract — 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. I.

© 2006 Zhao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

© 2005 Dunn et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

Research article Universal partitioning of the hierarchical fold network of 50-residue segments in proteins