發(fā)布時(shí)間：2019-04-18 22:29

【摘要】：對(duì)節(jié)點(diǎn)帶有內(nèi)容的信息網(wǎng)絡(luò)進(jìn)行語義社區(qū)發(fā)現(xiàn)是新的研究方向。融合節(jié)點(diǎn)內(nèi)容增加了算法的復(fù)雜度。提出了一種在線性時(shí)間內(nèi)進(jìn)行語義社區(qū)發(fā)現(xiàn)的標(biāo)簽傳播算法,用LDA(latent Dirichlet allocation)主題模型表示節(jié)點(diǎn)內(nèi)容,以節(jié)點(diǎn)內(nèi)容相似度和傳播影響力的乘性模型作為標(biāo)簽傳播的策略,在歸一化過程中,自然融合節(jié)點(diǎn)內(nèi)容和網(wǎng)絡(luò)結(jié)構(gòu)信息,標(biāo)簽迭代過程中,采用節(jié)點(diǎn)與絕大部分鄰居節(jié)點(diǎn)內(nèi)容不相同才進(jìn)行更新的策略,保證算法的運(yùn)行效率。通過在不同規(guī)模的12個(gè)真實(shí)數(shù)據(jù)集上進(jìn)行實(shí)驗(yàn),以模塊度和純度作為度量標(biāo)準(zhǔn),驗(yàn)證了算法在語義社區(qū)發(fā)現(xiàn)上的有效性和可行性。
[Abstract]:Semantic community discovery of node information networks with content is a new research direction. The fusion node content increases the complexity of the algorithm. In this paper, a label propagation algorithm for semantic community discovery in linear time is proposed. The node content is represented by LDA (latent Dirichlet allocation) topic model, and the multiplicative model of node content similarity and propagation influence is used as the strategy of tag propagation. In the process of normalization, the content of nodes and the information of network structure are merged naturally. In the process of label iteration, the updating strategy is adopted to ensure the running efficiency of the algorithm by adopting the strategy that the contents of nodes and most of the neighboring nodes are not the same. The effectiveness and feasibility of the algorithm in semantic community discovery are verified by experiments on 12 real data sets of different scales, taking modularity and purity as metrics.
【作者單位】： 北京聯(lián)合大學(xué)商務(wù)學(xué)院;中國人民大學(xué)信息學(xué)院;北京交通大學(xué)計(jì)算機(jī)與信息技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金Nos.71572015,71271209 北京聯(lián)合大學(xué)新起點(diǎn)項(xiàng)目No.Zk10201506~~
【分類號(hào)】：TP301.6
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本文編號(hào)：2460345