【摘要】：針對(duì)傳統(tǒng)社區(qū)發(fā)現(xiàn)算法多數(shù)是基于單一關(guān)系的同構(gòu)學(xué)術(shù)社會(huì)網(wǎng)絡(luò),而包含多種關(guān)系的異構(gòu)學(xué)術(shù)網(wǎng)絡(luò)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(jié)構(gòu)洞的學(xué)術(shù)社區(qū)發(fā)現(xiàn)算法——HAFCD算法。從構(gòu)建基于DBWorld郵件數(shù)據(jù)的異構(gòu)學(xué)術(shù)網(wǎng)絡(luò)出發(fā),通過(guò)分析異構(gòu)網(wǎng)絡(luò)中的多種關(guān)聯(lián)關(guān)系和節(jié)點(diǎn)內(nèi)容的相似性,提出改進(jìn)的語(yǔ)義路徑模型,計(jì)算評(píng)審人間的相似度。基于此,該算法根據(jù)結(jié)構(gòu)洞越少、網(wǎng)絡(luò)閉合性越高這一事實(shí),將結(jié)構(gòu)洞理論融入FCM算法進(jìn)行異構(gòu)學(xué)術(shù)社區(qū)發(fā)現(xiàn)。通過(guò)與現(xiàn)有的譜聚類和路徑選擇聚類算法進(jìn)行實(shí)驗(yàn)比較表明,該算法具有較好的計(jì)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(xué)光電信息與計(jì)算機(jī)工程學(xué)院;
【基金】：上海智能家居大規(guī)模物聯(lián)共性技術(shù)工程中心資助項(xiàng)目(GCZX14014) 滬江基金研究基地專項(xiàng)項(xiàng)目(C14001) 國(guó)家自然科學(xué)基金資助項(xiàng)目(61003031)
【分類號(hào)】：TP311.13

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