VizLattes: a tool for relevance analysis from scientific co-authorship networks

Social network data typically carry attribute information associated with the individuals and to their relationships. Such interconnected information can be useful to identify groups of individuals sharing common attribute properties and also to investigate the behaviour of particular individuals in a global network scenario. Different approaches have been introduced to extract and identify information of interest in social networks, and community identification is one of them. Some methods focus on identifying groups or communities of individuals based on their relationships, while others try to identify groups of individuals based on the common information they share. Integrating both approaches is not straightforward, as different mathematical and computational must be implemented and integrated into a unified framework. In this paper we approach this problem and propose a new method to identify underlying communities in a network, while highlighting the information shared by their components. Our solution relies on a single unified mathematical method. As a proof-of-concept, we have applied the proposed method to scientific co-authorship networks extracted from the wellknown Lattes Platform made available by CNPq, the Brazilian national science funding agency. We use textual information on the co-authors and their papers as focus attributes. We show that the method supports both community detection and also the identification of thematic paths, underlying topics and relevant authors characterizing distinct academic communities. The results presented show that this approach can be quite useful for exploration and understanding of academic collaboration networks.CAPESCNPqFAPES

Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18 module definitions, 129 different modularization methods, 13 module comparision methods) and 396 references. All algorithms can be downloaded from this web-site: http://www.linkgroup.hu/modules.ph