Investigating nanomaterial toxicity bibliography: A network analysis approach

The selection and prioritization of research directions are always challenges. This paper aims to make sense of nanomaterial toxicity publication and keywords data through quantitative metrics and network visualization. We have adapted a combined approach of network analysis, co-occurrence analysis, clustering analysis and visual analytics, to characterize important relational properties of network structures and features of entities. The results show that both co-authorship network and keywords network on nanomaterial toxicity follow the power-law degree distribution. In addition, the co-authorship network appears to be of scale-free pattern. We also investigate and visualize the research trends in field of nanomaterial toxicity by studying top influence researchers and keywords over years. These findings offer researchers various insights of the patterns and trends in the nanomaterial toxicity.

[1]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[2]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[3]  A. Barabasi,et al.  Scale-free characteristics of random networks: the topology of the world-wide web , 2000 .

[4]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[5]  R. Albert.  Scale-free networks in cell biology , 2005, Journal of Cell Science.

[6]  T. Xia,et al.  Toxic Potential of Materials at the Nanolevel , 2006, Science.

[7]  Loet Leydesdorff,et al.  Betweenness centrality as an indicator of the interdisciplinarity of scientific journals , 2007, J. Assoc. Inf. Sci. Technol..

[8]  Nancy A. Monteiro-Riviere,et al.  Nanotoxicology : characterization, dosing and health effects , 2007 .

[9]  Alessandro Vespignani,et al.  Network science , 2007, Annu. Rev. Inf. Sci. Technol..

[10]  Ulrik Brandes,et al.  On Modularity Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.

[11]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

[12]  Soundar Kumara,et al.  Complex Networks: An Engineering View , 2010, IEEE Circuits and Systems Magazine.

[13]  P. Stroeve,et al.  Toxicity of nanomaterials. , 2012, Chemical Society reviews.

[14]  A. Barabasi,et al.  Structural genomics: Open collaboration is key to new drugs , 2012, Nature.

[15]  Loet Leydesdorff,et al.  Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks , 2011, J. Informetrics.

[16]  M. Newman,et al.  Coauthorship and citation patterns in the Physical Review. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Kaizhi Tang,et al.  Predictive modeling of nanomaterial exposure effects in biological systems , 2013, International journal of nanomedicine.

[18]  Kaizhi Tang,et al.  NEIMiner: nanomaterial environmental impact data miner , 2013, International journal of nanomedicine.