基于圖正則化非負(fù)矩陣分解的二分網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn)算法
doi: 10.11999/JEIT141649
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解放軍信息工程大學(xué)信息系統(tǒng)工程學(xué)院 鄭州 450002
基金項目:
國家863計劃項目(2011AA013603)和國家重大科技專項(2013ZX 03006002)
Identifying Community in Bipartite Networks Using Graph Regularized-based Non-negative Matrix Factorization
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摘要: 現(xiàn)實(shí)世界存在大量二分網(wǎng)絡(luò),研究其社區(qū)結(jié)構(gòu)有助于從新角度認(rèn)識和理解異質(zhì)復(fù)雜網(wǎng)絡(luò)。非負(fù)矩陣分解模型能夠克服二分結(jié)構(gòu)的限制,有效地挖掘二分網(wǎng)絡(luò)的潛在結(jié)構(gòu),但也存在著時間復(fù)雜度高、收斂慢等問題。該文提出一種基于圖正則化的三重非負(fù)矩陣分解(NMTF)算法應(yīng)用于二分網(wǎng)絡(luò)社區(qū)發(fā)現(xiàn),通過圖正則化將用戶子空間和目標(biāo)子空間的內(nèi)部連接關(guān)系作為約束項引入到三重非負(fù)矩陣分解模型中;同時將NMTF分解為兩個最小化近似誤差的子問題,并給出了乘性迭代算法以交替更新因子矩陣,從而簡化矩陣分解迭代,加快收斂速度。實(shí)驗(yàn)和分析證明:對于計算機(jī)生成網(wǎng)絡(luò)和真實(shí)網(wǎng)絡(luò),該文提出的社區(qū)劃分方法均表現(xiàn)出較高的準(zhǔn)確率和穩(wěn)定性,能夠快速準(zhǔn)確地挖掘二分網(wǎng)絡(luò)的社區(qū)結(jié)構(gòu)。
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關(guān)鍵詞:
- 二分網(wǎng)絡(luò) /
- 社區(qū)發(fā)現(xiàn) /
- 圖正則化 /
- 非負(fù)矩陣分解
Abstract: There are many bipartite networks composed of two types of nodes in the real world, studying the community structure of them is helpful to understand the complex network from a new point of view. Non- negative matrix factorization can overcome the limitation of the two-mode structure of bipartite networks, but it is also subject to several problems such as slow convergence and large computation. In this paper, a novel algorithm using graph regularized-based non-negative matrix factorization is presented for community detection in bipartite networks. It respectively introduces the internal connecting information of two-kinds of nodes into the Non- negative Matrix Tri-Factorization (NMTF) model as the graph regularizations. Moreover, this paper divides NMTF into two sub problems of minimizing the approximation error, and presents an alternative iterative algorithm to update the factor matrices, thus the iterations of matrix factorization can be simplified and accelerated. Through the experiments on both computer-generated and real-world networks, the results and analysis show that the proposed method has superior performances than the typical community algorithms in terms of the accuracy and stability, and can effectively discover the meaningful community structures in bipartite networks. -
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