情报学报
情報學報
정보학보
2014年
8期
870-880
,共11页
权威度%学术实体%异构网络%评估%MapReduce
權威度%學術實體%異構網絡%評估%MapReduce
권위도%학술실체%이구망락%평고%MapReduce
authority%academic entity%heterogeneous network%evaluation%MapReduce
面对海量的科技文献资源，如何评估文献、作者和研究机构的学术质量和可信度引起了广泛关注。在众多可信度评价标准中，权威度是优先和关键的评价指标。因此，对科技文献、作者和机构等学术实体的科技实力和权威度进行研究与量化评估具有很大的现实意义。本文利用文献、作者、机构等三类实体间的引用、合著、合作等关系建立异构网络模型，在此基础上提出了混合随机游走算法 Co-AcademicRank 定量计算文献、作者、机构的权威度，并基于 MapReduce 实现了分布式的 Co-AcademicRank 算法。最后通过对情报学和图书馆学数据集测试与分析，对比分析了 PageRank 和 Co-ranking 算法，验证了本模型的有效性、准确性和优越性。同时，实验比较了算法在单机环境下和 Hadoop 平台下的运行时间，证明了分布式算法的高效性和稳定性。
麵對海量的科技文獻資源，如何評估文獻、作者和研究機構的學術質量和可信度引起瞭廣汎關註。在衆多可信度評價標準中，權威度是優先和關鍵的評價指標。因此，對科技文獻、作者和機構等學術實體的科技實力和權威度進行研究與量化評估具有很大的現實意義。本文利用文獻、作者、機構等三類實體間的引用、閤著、閤作等關繫建立異構網絡模型，在此基礎上提齣瞭混閤隨機遊走算法 Co-AcademicRank 定量計算文獻、作者、機構的權威度，併基于 MapReduce 實現瞭分佈式的 Co-AcademicRank 算法。最後通過對情報學和圖書館學數據集測試與分析，對比分析瞭 PageRank 和 Co-ranking 算法，驗證瞭本模型的有效性、準確性和優越性。同時，實驗比較瞭算法在單機環境下和 Hadoop 平檯下的運行時間，證明瞭分佈式算法的高效性和穩定性。
면대해량적과기문헌자원，여하평고문헌、작자화연구궤구적학술질량화가신도인기료엄범관주。재음다가신도평개표준중，권위도시우선화관건적평개지표。인차，대과기문헌、작자화궤구등학술실체적과기실력화권위도진행연구여양화평고구유흔대적현실의의。본문이용문헌、작자、궤구등삼류실체간적인용、합저、합작등관계건립이구망락모형，재차기출상제출료혼합수궤유주산법 Co-AcademicRank 정량계산문헌、작자、궤구적권위도，병기우 MapReduce 실현료분포식적 Co-AcademicRank 산법。최후통과대정보학화도서관학수거집측시여분석，대비분석료 PageRank 화 Co-ranking 산법，험증료본모형적유효성、준학성화우월성。동시，실험비교료산법재단궤배경하화 Hadoop 평태하적운행시간，증명료분포식산법적고효성화은정성。
In the face of huge amount resource of scientific literature,how to evaluate the quality and credibility of literatures along with related authors and research institutions has aroused widespread concern.Among various quality and credibility evaluation standards,the authority is a significant measurement with higher priority.Hence it has an important and practical significance to study and quantitatively evaluate the authority of various academic entities such as literature,author and research institution.In this paper,a heterogeneous relationship network model of academic entity is proposed by analyzing literatures citation, co-authorship and the co-operation relationship among academic institutions.Furthermore,a distributed hybrid random walk algorithm named Co-AcademicRank,which can quantitatively calculate the authority of literature, author and research institution simultaneously,is designed and implemented on the basis of Map-Reduce framework. At last, intensive experiments are performed with the dataset in the domain of Information Science and Library Science. The experiment result shows that Co-AcademicRank is more accurate and effective when comparing with the other algorithms such as PageRank and Co-Ranking.At meanwhile,the comparison of the elapsed time of the algorithm running in the single-computer environment and under the platform of Hadoop/Map-Reduce respectively also proves the high efficiency of the distributed Co-AcademicRank algorithm.
