發(fā)布時(shí)間：2018-03-12 22:06

  本文選題：復(fù)雜網(wǎng)絡(luò)　切入點(diǎn)：社團(tuán)發(fā)現(xiàn)　出處：《復(fù)雜系統(tǒng)與復(fù)雜性科學(xué)》2017年03期 　論文類型：期刊論文

【摘要】：傳統(tǒng)的局部適應(yīng)度社團(tuán)發(fā)現(xiàn)算法(LFM)在社團(tuán)結(jié)構(gòu)模糊的網(wǎng)絡(luò)中精度下降嚴(yán)重。針對(duì)此問題,提出LFMJ算法。利用鄰居節(jié)點(diǎn)信息和改進(jìn)的杰卡德系數(shù)重構(gòu)網(wǎng)絡(luò),使網(wǎng)絡(luò)結(jié)構(gòu)更為清楚,社團(tuán)劃分結(jié)果更為準(zhǔn)確。為驗(yàn)證算法,選擇了5種算法在LFR網(wǎng)絡(luò)和真實(shí)網(wǎng)絡(luò)中進(jìn)行測(cè)試,包括LFMJ、LFM和傳統(tǒng)的LPA算法以及性能較好的WT和FUA算法。結(jié)果表明:在標(biāo)準(zhǔn)LFR網(wǎng)絡(luò)中,LFMJ精度高于LFM和LPA,與FUA和WT相當(dāng);在真實(shí)網(wǎng)絡(luò)和具有重疊結(jié)構(gòu)的LFR網(wǎng)絡(luò)中,LFMJ精度優(yōu)于其他4種算法。
[Abstract]:The traditional local fitness community discovery algorithm (LFM) has a serious decline in precision in the network with fuzzy community structure. Aiming at this problem, LFMJ algorithm is proposed to reconstruct the network by using neighbor node information and improved Jekard coefficient to make the network structure clearer. In order to verify the algorithm, five algorithms are selected to test in LFR network and real network. The results show that in the standard LFR network, the accuracy of LFMJ is higher than that of LFM and LPA, which is equivalent to that of FUA and WT. In real network and LFR network with overlapping structure, the accuracy of LFMJ is better than the other four algorithms.
【作者單位】： 信息工程大學(xué)理學(xué)院理學(xué)院;河南中醫(yī)學(xué)院第一附屬醫(yī)院呼吸科;
【基金】：國家自然科學(xué)基金(81574100)
【分類號(hào)】：O157.5;TP301.6
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本文編號(hào)：1603488