【摘要】：學(xué)術(shù)社會網(wǎng)絡(luò)是通過學(xué)術(shù)活動構(gòu)建起來的網(wǎng)絡(luò),學(xué)者組成了網(wǎng)絡(luò)中的各個節(jié)點,學(xué)者之間的合著關(guān)系構(gòu)成了網(wǎng)絡(luò)中的邊。隨著學(xué)術(shù)研究越來越快的發(fā)展,學(xué)術(shù)社會網(wǎng)絡(luò)的規(guī)模也逐漸增大。在規(guī)模如此巨大的學(xué)術(shù)社會網(wǎng)絡(luò)中搜索需要的信息,屬于比較前沿的研究方向。目前,已經(jīng)有很多學(xué)者對學(xué)術(shù)社會網(wǎng)絡(luò)搜索進(jìn)行了研究,也已經(jīng)取得了階段性的進(jìn)展。將這種學(xué)術(shù)搜索付諸實踐,論文審稿人評選就是其中一個典型的應(yīng)用。它是在專家搜索的基礎(chǔ)之上考慮審稿人與被審稿人之間的社會關(guān)系,從而搜索出符合條件的審稿人。為了解決學(xué)術(shù)社會網(wǎng)絡(luò)搜索問題,本文提出一種基于關(guān)聯(lián)度分析的學(xué)術(shù)社會網(wǎng)絡(luò)搜索算法。本研究課題提出的算法主要內(nèi)容包括:首先,需要計算候選節(jié)點與查詢節(jié)點之間的內(nèi)容相似度,這里采用的是短文本相似度的計算方法。為了使得計算出來的內(nèi)容相似度更加全面,也更加符合實際情況,本文提出一種基于鄰居節(jié)點語義關(guān)聯(lián)度的短文本相似度計算方法,能解決之前短文本相似度計算方法存在的不足;其次,需要計算候選節(jié)點與查詢節(jié)點之間的結(jié)構(gòu)相似度,它是利用節(jié)點之間的最短路徑來表示的,由于本文研究的網(wǎng)絡(luò)圖屬于無向無權(quán)圖,因此可以采用廣度優(yōu)先遍歷的方法計算最短路徑;然后,計算候選節(jié)點的權(quán)威度。綜合上面的三個因子,構(gòu)造候選節(jié)點與查詢節(jié)點之間的關(guān)聯(lián)度模型。最后,采用隨機(jī)游走搜索策略進(jìn)行節(jié)點的搜索,為了使得搜索過程更加快速準(zhǔn)確,提出基于最短路徑的隨機(jī)游走搜索策略。這樣,經(jīng)過上述過程每個節(jié)點都會有一個分值,根據(jù)分值的高低為候選節(jié)點排序,選擇指定數(shù)目的節(jié)點返回給用戶。本文使用C-DBLP的數(shù)據(jù)集對搜索算法進(jìn)行性能測試。實驗結(jié)果表明,基于關(guān)聯(lián)度分析的學(xué)術(shù)社會網(wǎng)絡(luò)搜索算法在各項性能指標(biāo)上比其他的搜索算法都有所提升,與之前的理論推斷相符合。
[Abstract]:Academic social network is a network constructed through academic activities. Scholars make up each node of the network, and the co-authorship between scholars constitutes the edge of the network. With the rapid development of academic research, the scale of academic social network is gradually increasing. Searching for the required information in such a large academic social network is a frontier research direction. At present, many scholars have carried on the research to the academic social network search, also has made the stage progress. To put this kind of academic search into practice, the selection of paper reviewers is one of the typical applications. It considers the social relationship between the reviewer and the reviewer on the basis of the expert search, so as to search the qualified reviewer. In order to solve the problem of academic social network search, this paper presents an academic social network search algorithm based on correlation analysis. The main contents of the algorithm are as follows: firstly, the content similarity between candidate nodes and query nodes should be calculated, and the method of calculating the similarity between candidate nodes and query nodes is adopted here. In order to make the content similarity calculated more comprehensive and more in line with the actual situation, this paper proposes a short text similarity calculation method based on neighbor node semantic correlation degree. It can solve the shortcomings of the previous similarity calculation method. Secondly, we need to calculate the structural similarity between candidate nodes and query nodes, which is represented by the shortest path between nodes, because the network graph studied in this paper belongs to the undirected unauthorized graph. Therefore, the shortest path can be calculated by using the breadth-first traversal method. Then, the authority of the candidate node is calculated. By synthesizing the above three factors, the correlation model between candidate node and query node is constructed. Finally, the random walk search strategy is used to search the nodes. In order to make the search process more rapid and accurate, a random walk search strategy based on the shortest path is proposed. In this way, each node will have a score after the above process, according to the value of the candidate node sort, select a specified number of nodes to return to the user. This paper uses the data set of C-DBLP to test the performance of the search algorithm. The experimental results show that the academic social network search algorithm based on correlation analysis has better performance than other search algorithms, which is consistent with the previous theoretical inference.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.3

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