【摘要】：針對傳統(tǒng)社區(qū)發(fā)現(xiàn)算法多數(shù)是基于單一關系的同構學術社會網(wǎng)絡,而包含多種關系的異構學術網(wǎng)絡社區(qū)發(fā)現(xiàn)算法還不多的情況,提出一種基于FCM(fuzzy C-means)和結構洞的學術社區(qū)發(fā)現(xiàn)算法——HAFCD算法。從構建基于DBWorld郵件數(shù)據(jù)的異構學術網(wǎng)絡出發(fā),通過分析異構網(wǎng)絡中的多種關聯(lián)關系和節(jié)點內容的相似性,提出改進的語義路徑模型,計算評審人間的相似度�；诖�,該算法根據(jù)結構洞越少、網(wǎng)絡閉合性越高這一事實,將結構洞理論融入FCM算法進行異構學術社區(qū)發(fā)現(xiàn)。通過與現(xiàn)有的譜聚類和路徑選擇聚類算法進行實驗比較表明,該算法具有較好的計算效果。
[Abstract]:Most of the traditional community discovery algorithms are isomorphic academic social networks based on a single relationship, but there are not many heterogeneous academic network community discovery algorithms including multiple relationships. This paper presents an algorithm for discovering academic community based on FCM (fuzzy C-means) and structure hole, which is called HAFCD algorithm. Based on the construction of heterogeneous academic network based on DBWorld email data, this paper proposes an improved semantic path model to calculate the similarity between reviewers by analyzing the various association relationships and the similarity of node content in heterogeneous networks. Based on the fact that the fewer the structure holes and the higher the network closeness, the structure hole theory is incorporated into the FCM algorithm for the discovery of the heterogeneous academic community. Compared with the existing spectral clustering and path selection clustering algorithms, the experimental results show that the proposed algorithm is effective.
【作者單位】： 上海理工大學光電信息與計算機工程學院;
【基金】：上海智能家居大規(guī)模物聯(lián)共性技術工程中心資助項目(GCZX14014) 滬江基金研究基地專項項目(C14001) 國家自然科學基金資助項目(61003031)
【分類號】：TP311.13

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