Bian Jiang, Xie Mengjun, Topaloglu Umit, Hudson Teresa, Eswaran Hari, Hogan William
Division of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Computer Science, University of Arkansas at Little Rock, Little Rock, AR 72204, USA.
J Biomed Inform. 2014 Dec;52:130-40. doi: 10.1016/j.jbi.2014.01.015. Epub 2014 Feb 18.
The popularity of social networks has triggered a number of research efforts on network analyses of research collaborations in the Clinical and Translational Science Award (CTSA) community. Those studies mainly focus on the general understanding of collaboration networks by measuring common network metrics. More fundamental questions about collaborations still remain unanswered such as recognizing "influential" nodes and identifying potential new collaborations that are most rewarding.
We analyzed biomedical research collaboration networks (RCNs) constructed from a dataset of research grants collected at a CTSA institution (i.e., University of Arkansas for Medical Sciences (UAMS)) in a comprehensive and systematic manner. First, our analysis covers the full spectrum of a RCN study: from network modeling to network characteristics measurement, from key nodes recognition to potential links (collaborations) suggestion. Second, our analysis employs non-conventional model and techniques including a weighted network model for representing collaboration strength, rank aggregation for detecting important nodes, and Random Walk with Restart (RWR) for suggesting new research collaborations.
By applying our models and techniques to RCNs at UAMS prior to and after the CTSA, we have gained valuable insights that not only reveal the temporal evolution of the network dynamics but also assess the effectiveness of the CTSA and its impact on a research institution. We find that collaboration networks at UAMS are not scale-free but small-world. Quantitative measures have been obtained to evident that the RCNs at UAMS are moving towards favoring multidisciplinary research. Moreover, our link prediction model creates the basis of collaboration recommendations with an impressive accuracy (AUC: 0.990, MAP@3: 1.48 and MAP@5: 1.522). Last but not least, an open-source visual analytical tool for RCNs is being developed and released through Github.
Through this study, we have developed a set of techniques and tools for analyzing research collaboration networks and conducted a comprehensive case study focusing on a CTSA institution. Our findings demonstrate the promising future of these techniques and tools in understanding the generative mechanisms of research collaborations and helping identify beneficial collaborations to members in the research community.
社交网络的普及引发了一系列针对临床与转化科学奖（CTSA）社区研究合作网络分析的研究工作。这些研究主要集中在通过测量常见的网络指标来对合作网络进行总体了解。关于合作的更基本问题仍然没有答案，例如识别“有影响力”的节点以及确定最有价值的潜在新合作。
我们以全面系统的方式分析了从CTSA机构（即阿肯色大学医学科学分校（UAMS））收集的研究资助数据集构建的生物医学研究合作网络（RCN）。首先，我们的分析涵盖了RCN研究的全范围：从网络建模到网络特征测量，从关键节点识别到潜在链接（合作）建议。其次，我们的分析采用了非常规模型和技术，包括用于表示合作强度的加权网络模型、用于检测重要节点的排名聚合以及用于建议新研究合作的带重启的随机游走（RWR）。
通过将我们的模型和技术应用于CTSA前后UAMS的RCN，我们获得了宝贵的见解，不仅揭示了网络动态的时间演变，还评估了CTSA的有效性及其对研究机构的影响。我们发现UAMS的合作网络不是无标度的，而是小世界的。已获得定量措施以证明UAMS的RCN正朝着有利于多学科研究的方向发展。此外，我们的链接预测模型以令人印象深刻的准确率（AUC：0.990，MAP@3：1.48和MAP@5：1.522）为合作推荐奠定了基础。最后但同样重要的是，正在通过Github开发并发布一个用于RCN的开源可视化分析工具。
通过这项研究，我们开发了一套用于分析研究合作网络的技术和工具，并针对一个CTSA机构进行了全面的案例研究。我们的研究结果表明，这些技术和工具在理解研究合作的生成机制以及帮助识别对研究社区成员有益的合作方面具有广阔的前景。
