Recent development of network structure analysis shows that it plays an important role in characterizing complex system of many branches of sciences. Different from previous network centrality measures, this paper proposes the notion of topological centrality (TC) reflecting the topological positions of nodes and edges in general networks, and proposes an approach to calculating the topological centrality. The proposed topological centrality is then used to discover communities and build the backbone network. Experiments and applications on research network show the significance of the proposed approach.
 Using data from computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We have studied many statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, typical distance through the network from one scientist to another, and a variety of measures of connectedness within a network, such as closeness and betweenness. We further argue that simple networks such as these cannot capture the variation in the strength of collaborative ties and propose a measure of this strength based on the number of papers coauthored by pairs of scientists, and the number of other scientists with whom they worked on those papers. Using a selection of our results, we suggest a variety of possible ways to answer the question, "Who is the best connected scientist?" Abstract Patrick Ion (Mathematical Reviews) and Jerry Grossman (Oakland University) maintain a collection of data on Paul Erdos, his co-authors and their co-authors. These data can be represented by a graph, also called the Erdos collaboration graph. In this paper, some techniques for analysis of large networks (different approaches to identify ‘interesting’ individuals and groups, analysis of internal structure of the main core using pre-specified blockmodeling and hierarchical clustering) and visualizations of their parts, are presented on the case of Erdos collaboration graph, using the program Pajek . Has sociology become more socially integrated over the last 30 years? Recent work in the sociology of knowledge demonstrates a direct linkage between social interaction patterns and the structure of ideas, suggesting that scientific collaboration networks affect scientific practice. I test three competing models for sociological collaboration networks and find that a structurally cohesive core that has been growing steadily since the early 1960s characterizes the discipline's coauthorship network. The results show that participation in the sociology collaboration network depends on research specialty and that quantitative work is more likely to be coauthored than non-quantitative work. However, structural embeddedness within the network core given collaboration is largely unrelated to specialty area. This pattern is consistent with a loosely overlapping specialty structure that has potentially integrative implications for theoretical development in sociology. Scientific collaboration has become a major issue in science policy. The tremendous growth of collaboration among nations and research institutions witnessed during the last twenty years is a function of the internal dynamics of science as well as science policy initiatives. The need to survey and follow up the collaboration issue calls for statistical indicators sensitive enough to reveal the structure and change of collaborative networks. In this context, bibliometric analysis of co-authored scientific articles is one promising approach. This paper discusses the relationship between collaboration and co-authorship, the nature of bibliometric data, and exemplifies how they can be refined and used to analyse various aspects of collaboration. The structure of scientific collaboration networks is investigated. Two scientists are considered connected if they have authored a paper together and explicit networks of such connections are constructed by using data drawn from a number of databases, including MEDLINE (biomedical research), the Los Alamos e-Print Archive (physics), and NCSTRL (computer science). I show that these collaboration networks form “small worlds,” in which randomly chosen pairs of scientists are typically separated by only a short path of intermediate acquaintances. I further give results for mean and distribution of numbers of collaborators of authors, demonstrate the presence of clustering in the networks, and highlight a number of apparent differences in the patterns of collaboration between the fields studied. 