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  • × author_ss:"He, B."
  • × author_ss:"Ding, Y."
  1. He, B.; Ding, Y.; Ni, C.: Mining enriched contextual information of scientific collaboration : a meso perspective (2011) 0.01 
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    Abstract
    Studying scientific collaboration using coauthorship networks has attracted much attention in recent years. How and in what context two authors collaborate remain among the major questions. Previous studies, however, have focused on either exploring the global topology of coauthorship networks (macro perspective) or ranking the impact of individual authors (micro perspective). Neither of them has provided information on the context of the collaboration between two specific authors, which may potentially imply rich socioeconomic, disciplinary, and institutional information on collaboration. Different from the macro perspective and micro perspective, this article proposes a novel method (meso perspective) to analyze scientific collaboration, in which a contextual subgraph is extracted as the unit of analysis. A contextual subgraph is defined as a small subgraph of a large-scale coauthorship network that captures relationship and context between two coauthors. This method is applied to the field of library and information science. Topological properties of all the subgraphs in four time spans are investigated, including size, average degree, clustering coefficient, and network centralization. Results show that contextual subgprahs capture useful contextual information on two authors' collaboration.
  2. Li, D.; Ding, Y.; Sugimoto, C.; He, B.; Tang, J.; Yan, E.; Lin, N.; Qin, Z.; Dong, T.: Modeling topic and community structure in social tagging : the TTR-LDA-Community model (2011) 0.01 
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    Abstract
    The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems.

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