本文選題：作者共引網(wǎng)絡(luò) + 共引網(wǎng)絡(luò)　； 參考：《圖書情報(bào)工作》2017年03期

【摘要】：[目的/意義]作者共引網(wǎng)絡(luò)分析(ACNA)是文獻(xiàn)計(jì)量學(xué)中的重要分析方法,旨在通過(guò)尋找學(xué)術(shù)文獻(xiàn)集合中作者之間的共引關(guān)系繪制出特定領(lǐng)域的知識(shí)圖譜,進(jìn)而指導(dǎo)科學(xué)研究。然而,ACNA的一個(gè)缺陷是其原始矩陣輸入信息量過(guò)小。本文通過(guò)提出作者混合共引網(wǎng)絡(luò)(HACNA),繪制更為精確的科學(xué)知識(shí)圖譜。[方法/過(guò)程]鑒于不同種類的學(xué)術(shù)網(wǎng)絡(luò)能為繪制知識(shí)圖譜提供不同維度的信息,提高知識(shí)圖譜繪制的精確性,本文以合著網(wǎng)絡(luò)和引用網(wǎng)絡(luò)為例,結(jié)合其他種類的學(xué)術(shù)網(wǎng)絡(luò)在ACNA基礎(chǔ)上進(jìn)行精確科學(xué)知識(shí)圖譜的繪制。[結(jié)果/結(jié)論]實(shí)證研究結(jié)果顯示,與ACNA相比,HACNA繪制出的知識(shí)圖譜在聚類過(guò)程中能夠使得同類作者更為聚攏、不同類作者更為分散,從而提高了聚類效果和可視化程度。同時(shí),HACNA繪制出的知識(shí)圖譜還能夠挖掘出更多細(xì)節(jié)。
[Abstract]:[objective / significance] the author cocitation network analysis (ACNA) is an important analytical method in bibliometrics, which aims to draw a map of knowledge in a specific field by looking for cocitation relationships among authors in a collection of academic documents, and then to guide scientific research. However, one of the defects of ACNA is that its original matrix input information is too small. A more accurate map of scientific knowledge is drawn through the author's hybrid cocitation network HACNA. [methods / processes] given that different types of academic networks can provide different dimensions of information for mapping knowledge maps and improve the accuracy of mapping knowledge maps, this paper takes coauthor networks and citation networks as examples. Combined with other types of academic networks to map accurate scientific knowledge on the basis of ACNA. [results / conclusion] the results of empirical study show that compared with ACNA, the knowledge map drawn by ACNA can make the same authors gather more closely, and the authors of different classes become more dispersed, thus improving the clustering effect and visualization degree. At the same time HACNA map of knowledge can also be mined out more details.
【作者單位】： 北京大學(xué)信息管理系;美國(guó)印第安納大學(xué)信息學(xué)與計(jì)算機(jī)學(xué)院;
【基金】：中國(guó)科技信息研究所系所合作項(xiàng)目研究成果之一
【分類號(hào)】：G353.1
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本文編號(hào)：1965299