[目的/意义] 判别重点研究方向对科研管理和科技政策的制定有着重要参考价值，已有的定量方法多是根据创新性、新颖性以及增长性等特征属性设计指标进行识别、推荐，本研究进一步利用研究方向间的关联关系，从网络拓扑结构和特征属性两个维度判别重点研究方向。[方法/过程] 在构建领域文献引用网络的基础上，利用大规模网络聚类算法识别研究方向，并构建研究方向关联网络，利用网络重要节点识别算法从网络拓扑结构的角度判别重点研究方向，同时结合新颖性、增长性和H指数三个特征属性指标，构建了重点研究方向遴选指标体系。[结果/结论] 对纳米科技领域进行实证分析，经专家判读，认为加权PageRank、Gefura以及增长性指标更加具有客观性、全面性和稳定性，通过综合运用三个指标遴选出208个纳米科技领域的重点研究方向。
[Purpose/significance] The identification of key research directions (KRDs) is significant to the management of scientific research and the formulation of policy. Existing methods of quantitative analysis mainly used indicators based on novelty, growth and other characteristics to identify and recommend KRDs. This paper used the relationships of research directions further and identified KRDs from network topology and characteristic two dimensions. [Method/process] On the basis of the building of citation network of papers in a field, a large-scale network clustering algorithm was used to detect research directions, and the topic association network was built. Then, identification algorithms of important nodes in complex networks were used to identify KRDs from the dimension of network topology, and an index system of selecting KRDs was constructed by combining three indicators of characteristic dimension, including novelty index, growth index and H-index. [Result/conclusion] After an empirical analysis of the field of nanotechnology, with the experts' interpretation, the paper concluded that growth index, Gefura and weighted PageRank are more objective and stable. Finally, 208 KRDs were identified by synthetically using above three indicators.
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