發(fā)布時(shí)間：2018-03-24 01:10

  本文選題：網(wǎng)絡(luò)嵌入　切入點(diǎn)：閉回路采樣　出處：《小型微型計(jì)算機(jī)系統(tǒng)》2017年09期

【摘要】：近年來,由于網(wǎng)絡(luò)數(shù)據(jù)規(guī)模膨脹而導(dǎo)致傳統(tǒng)的網(wǎng)絡(luò)挖掘模型效率低下的現(xiàn)象,使得網(wǎng)絡(luò)嵌入模型成為當(dāng)前社會(huì)網(wǎng)絡(luò)分析的熱點(diǎn).不同于以往模型的隨機(jī)采樣方式,本文考慮閉合回路機(jī)制對結(jié)點(diǎn)采樣序列的影響,提出一種閉回路采樣的網(wǎng)絡(luò)嵌入模型,能夠?qū)⒋笠?guī)模網(wǎng)絡(luò)中結(jié)點(diǎn)的結(jié)構(gòu)特征映射到連續(xù)的、低維度的向量空間.這樣學(xué)習(xí)到的結(jié)點(diǎn)特征向量能夠更好地反應(yīng)網(wǎng)絡(luò)的真實(shí)結(jié)構(gòu)特性,并且可以很容易地應(yīng)用到網(wǎng)絡(luò)數(shù)據(jù)挖掘的分類、推薦和預(yù)測等任務(wù).本文選取3個(gè)真實(shí)網(wǎng)絡(luò)數(shù)據(jù)集進(jìn)行多標(biāo)簽分類和聚類的實(shí)驗(yàn),并與多個(gè)最新的基準(zhǔn)方法對比,結(jié)果驗(yàn)證了該方法能夠?qū)W習(xí)到更好的結(jié)點(diǎn)特征向量.
[Abstract]:In recent years, due to the expansion of scale and network data led to the traditional network mining model is inefficient, making the network embedded model has become a hot topic in the social network analysis. Random sampling method is different from the previous model, this paper considers the influence mechanism of closed loop sampling sequence of nodes, this paper proposes a model of close loop sampling network embedding that can be a large network structure characteristic of node is mapped to the continuous, low dimensional vector space. This node feature vector to learn to better reflect the network structure characteristics, classification and can be easily applied to network data mining, recommendation and prediction tasks. This paper selects 3 real network data set of multi label classification and clustering experiments, and compared with a new reference method, results show that this method can learn better node Eigenvector.

【作者單位】： 武漢大學(xué)計(jì)算機(jī)學(xué)院;漢口學(xué)院計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61272277)資助 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)基金項(xiàng)目(274742)資助
【分類號】：O157.5
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本文編號：1656025