本文選題：群智感知　切入點(diǎn)：社會(huì)關(guān)系量化　出處：《西安交通大學(xué)學(xué)報(bào)》2017年06期 　論文類型：期刊論文

【摘要】：針對(duì)群智感知中節(jié)點(diǎn)社會(huì)關(guān)系計(jì)算存在的層次關(guān)系劃分不清、關(guān)聯(lián)因子描述粗糙等問(wèn)題,提出一種采用社會(huì)屬性的親密度量化方法。該方法通過(guò)分析影響節(jié)點(diǎn)社會(huì)關(guān)系的多維要素,將節(jié)點(diǎn)屬性因子劃分為靜態(tài)和動(dòng)態(tài)兩個(gè)維度。通過(guò)構(gòu)造多維語(yǔ)義分級(jí)樹(shù)和空間索引編碼,對(duì)節(jié)點(diǎn)靜態(tài)屬性進(jìn)行挖掘和形式化表示。同時(shí),引入交互信息熵,對(duì)社會(huì)關(guān)系的不對(duì)稱性進(jìn)行分析和比較,以提高親密度量化方法的客觀性。基于層次分析法實(shí)現(xiàn)節(jié)點(diǎn)動(dòng)態(tài)屬性的二級(jí)評(píng)判和有效聚合,并通過(guò)節(jié)點(diǎn)融合度對(duì)親密關(guān)系進(jìn)行二次修正。實(shí)驗(yàn)結(jié)果表明:與已有方法相比,采用社會(huì)屬性的親密度量化方法預(yù)測(cè)準(zhǔn)確率提高了14.67%,該方法能夠有效降低群智感知中移動(dòng)節(jié)點(diǎn)的誤判概率,提高網(wǎng)絡(luò)社團(tuán)識(shí)別準(zhǔn)確率,為候選服務(wù)節(jié)點(diǎn)集的選擇提供有效依據(jù)。
[Abstract]:In order to solve the problems such as unclear classification of hierarchical relations and rough description of correlation factors in the computation of social relations between nodes in the perception of group intelligence, and so on, In this paper, a quantitative method of affinity using social attributes is proposed, which is based on the analysis of the multi-dimensional factors that affect the social relations of nodes. The node attribute factor is divided into static and dynamic dimensions. By constructing multi-dimensional semantic hierarchical tree and spatial index coding, the static attributes of nodes are mined and formally represented. At the same time, interactive information entropy is introduced. The asymmetry of social relations is analyzed and compared in order to improve the objectivity of the method of affinity quantification. Based on the analytic hierarchy process (AHP), the two-level evaluation and effective aggregation of node dynamic attributes are realized. The experiment results show that: compared with the existing methods, The prediction accuracy rate of social attribute based affinity quantization method is improved by 14.67. this method can effectively reduce the misjudgment probability of mobile nodes in group intelligence perception, improve the accuracy of network community identification, and provide an effective basis for the selection of candidate service node sets.
【作者單位】： 西安交通大學(xué)電子與信息工程學(xué)院;新疆大學(xué)信息科學(xué)與工程學(xué)院;西安交通大學(xué)陜西省計(jì)算機(jī)網(wǎng)絡(luò)重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61502380) 陜西省自然科學(xué)基金資助項(xiàng)目(2014JQ8322) 陜西省科技統(tǒng)籌創(chuàng)新工程資助項(xiàng)目(2013SZS16-Z01/P01/K01) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金資助項(xiàng)目(XJJ2014049)
【分類號(hào)】：C912.3

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