ID 原文 译文
52637 基于兴趣点(POI)，采用图论中判定节点重要度的方法，根据节点在路网中的基本信息，建立一种判定节点重要度的通用节点重要度(GNI)算法。 Based on point of interest(POI) and adopting the method of determining the importance of nodes in graph theory, a general node importance(GNI) algorithm for determining the importance of nodes is established according to the basic information of the nodes in the road network.
52638 针对不同场所出租车需求量不同的问题，建立了一种不同模式下的出租车按需分配呼叫模型。 Aiming at the problem of the different demand for taxis in different places, an on-demand taxi call model with different modes is established.
52639 以GNI算法为基础，获得基于POI的专用节点重要度(DNI)算法。 Based on the GNI algorithm, a dedicated node importance(DNI) algorithm based on POI is obtained.
52640 采用杠杆原理，同时类比多边形获取“重心”的方法，分析DNI算法下节点重要度排名结果，获得出租车最佳停靠位置，通过实验验证了该DNI算法的实用性及有效性。 Using the principle of leverage and making an analogy with the method of obtaining the ‘center of gravity' for polygons, the ranking results of node importance under the DNI algorithm are analyzed to obtain the best taxi parking stand, and the practicality and effectiveness of the DNI algorithm are verified through experiments.
52641 科研主题识别和演化分析是了解科研领域发展现状和趋势的重要手段。 Identification and analysis of the evolution state of scientific research topics are important means to understand the current situation and trend of research topics.
52642 近年来，基于复杂网络和网络演化计量的社区发现算法成为揭示科研主题演化的主要方法，有必要深入探讨该类算法在主题识别和演化分析中的优点与不足。 In recent years, community discovery algorithm based on complex networks and network evolution metrics has become one of the main methods to reveal scientific research topics. Therefore, it is necessary to explore the advantages and disadvantages of these algorithms in topic discovery and evolution research.
52643 本文选取植物油菜素甾醇领域，构建了关键词共现网络，对比了Newman MM、Ball Overlapping和Blondel这3种典型社区发现算法的科研主题识别和演化追踪结果， Brassinosteroids hormone research area is selected as an example, the keyword co-occurrence network in this field is constructed, and the performances of three typical community discovery algorithms(Newman MM, Ball Overlapping and Blondel) in scientific research topic recognition are compared;
52644 并结合专家知识对3种算法效果进行分析。 the accuracy of the detection of research topic and the evolutionary analysis results is judged by expert knowledge.
52645 实验结果表明，Blondel算法在科研主题的识别和演化追踪研究中运算速度最快，识别的主题更为准确，能够较好反映领域主题演化。 The results show that the Blondel algorithm has great advantages in the identification and evolution tracking of scientific research topics. It has not only the fastest speed of operation, but also the more accurate topic recognition.
52646 该研究从算法适用性角度为科研主题演化追踪研究提供了参考。 It provides a reference for the evolution tracking of scientific research topics from the perspective of algorithm applicability.