復(fù)雜網(wǎng)絡(luò)重要節(jié)點識別方法研究
發(fā)布時間：2018-10-10 16:28
【摘要】：復(fù)雜網(wǎng)絡(luò)是一門新興的交叉學(xué)科,近年來一直活躍在科研的各個領(lǐng)域。在自然界中,絕大多數(shù)復(fù)雜系統(tǒng)都可以抽象成網(wǎng)絡(luò),一般由節(jié)點、邊、權(quán)重等基本單元構(gòu)成。在復(fù)雜網(wǎng)絡(luò)中能夠從很大程度上影響網(wǎng)絡(luò)的抗毀性和傳播、同步、控制等功能的節(jié)點被稱為重要節(jié)點。隨著網(wǎng)絡(luò)規(guī)模的增大和網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)日趨復(fù)雜,合理且精準(zhǔn)地評價節(jié)點重要性是復(fù)雜網(wǎng)絡(luò)研究中的一個重要問題。本文主要對復(fù)雜網(wǎng)絡(luò)節(jié)點重要性排序和重要節(jié)點挖掘兩部分內(nèi)容開展研究:1.針對現(xiàn)有節(jié)點重要性排序算法時間復(fù)雜度較高,排序機理比較單一的問題,提出了一種基于膨脹率抽樣的節(jié)點重要性排序算法,該算法能夠發(fā)現(xiàn)網(wǎng)絡(luò)中度中心性較小但位于不同社區(qū)之間的橋接節(jié)點,此類節(jié)點在信息傳播的速度和擴散范圍上具備獨有的位置優(yōu)勢。仿真實驗表明,排序結(jié)果具有較高的識別精度,算法時間復(fù)雜度較低,而且能夠發(fā)現(xiàn)一些被其他算法同時忽略掉的重要節(jié)點。2.針對信息傳播最大化的Top-k節(jié)點挖掘算法時間復(fù)雜度高,傳播范圍重疊的問題,提出了一種基于節(jié)點局部信息指數(shù)的挖掘算法,將初始種子節(jié)點分布在合理位置,使其在傳播過程中規(guī)避富人俱樂部現(xiàn)象,降低重疊傳播的損耗。仿真實驗表明,挖掘出的種子節(jié)點組合傳播力較強,且算法時間復(fù)雜度為線性,運算時間非常短。本文致力于重要節(jié)點識別方法的研究,提出了基于膨脹率的節(jié)點排序算法和基于局部信息的Top-k節(jié)點挖掘算法。較現(xiàn)有算法運算速度更快,且排序和挖掘精度能夠達(dá)到或超過同類算法水平。補充和完善了重要節(jié)點識別算法研究體系,提升了算法性能。本研究在網(wǎng)絡(luò)信息挖掘方面具備積極的理論研究意義,且研究成果可以較好地應(yīng)用于社交網(wǎng)絡(luò)、生物信息、電力網(wǎng)絡(luò)等實際應(yīng)用領(lǐng)域,有較高的應(yīng)用價值。
[Abstract]:Complex network is a new interdisciplinary subject, which has been active in various fields of scientific research in recent years. In nature, most complex systems can be abstracted into networks, which are generally composed of nodes, edges, weights and other basic units. Nodes that can greatly affect the survivability, propagation, synchronization, control and other functions of complex networks are called important nodes. With the increase of network scale and the increasing complexity of network topology, it is an important problem to evaluate node importance reasonably and accurately. In this paper, the importance of the complex network node ranking and important node mining two parts of research: 1. Aiming at the problem of high time complexity and single sorting mechanism of existing node importance sorting algorithms, a node importance sorting algorithm based on expansion rate sampling is proposed. The algorithm can find the bridging nodes with moderate centrality but located between different communities, which have unique location advantages in the speed and spread range of information dissemination. Simulation results show that the sorting results have higher recognition accuracy, lower time complexity, and can find some important nodes neglected by other algorithms. 2. Aiming at the problem of high time complexity and overlapping propagation range of Top-k node mining algorithm with maximum information dissemination, a mining algorithm based on local information index of nodes is proposed, which distributes the initial seed nodes in a reasonable position. Make it avoid the rich club phenomenon in the process of communication, reduce the loss of overlapping transmission. The simulation results show that the combined propagation power of the extracted seed nodes is strong, and the time complexity of the algorithm is linear, and the operation time is very short. In this paper, we focus on the research of important node recognition methods, and propose a node sorting algorithm based on expansion ratio and a Top-k node mining algorithm based on local information. The algorithm is faster than the existing algorithms, and the precision of sorting and mining can reach or exceed the level of similar algorithms. It complements and perfects the research system of important node recognition algorithm and improves the performance of the algorithm. This research has positive theoretical significance in the field of network information mining, and the research results can be applied to social networks, biological information, power networks and other practical applications, which has a higher application value.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O157.5
[Abstract]:Complex network is a new interdisciplinary subject, which has been active in various fields of scientific research in recent years. In nature, most complex systems can be abstracted into networks, which are generally composed of nodes, edges, weights and other basic units. Nodes that can greatly affect the survivability, propagation, synchronization, control and other functions of complex networks are called important nodes. With the increase of network scale and the increasing complexity of network topology, it is an important problem to evaluate node importance reasonably and accurately. In this paper, the importance of the complex network node ranking and important node mining two parts of research: 1. Aiming at the problem of high time complexity and single sorting mechanism of existing node importance sorting algorithms, a node importance sorting algorithm based on expansion rate sampling is proposed. The algorithm can find the bridging nodes with moderate centrality but located between different communities, which have unique location advantages in the speed and spread range of information dissemination. Simulation results show that the sorting results have higher recognition accuracy, lower time complexity, and can find some important nodes neglected by other algorithms. 2. Aiming at the problem of high time complexity and overlapping propagation range of Top-k node mining algorithm with maximum information dissemination, a mining algorithm based on local information index of nodes is proposed, which distributes the initial seed nodes in a reasonable position. Make it avoid the rich club phenomenon in the process of communication, reduce the loss of overlapping transmission. The simulation results show that the combined propagation power of the extracted seed nodes is strong, and the time complexity of the algorithm is linear, and the operation time is very short. In this paper, we focus on the research of important node recognition methods, and propose a node sorting algorithm based on expansion ratio and a Top-k node mining algorithm based on local information. The algorithm is faster than the existing algorithms, and the precision of sorting and mining can reach or exceed the level of similar algorithms. It complements and perfects the research system of important node recognition algorithm and improves the performance of the algorithm. This research has positive theoretical significance in the field of network information mining, and the research results can be applied to social networks, biological information, power networks and other practical applications, which has a higher application value.
【學(xué)位授予單位】：河南師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O157.5
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 曹玖新;董丹;徐順;鄭嘯;劉波;羅軍舟;;一種基于k-核的社會網(wǎng)絡(luò)影響最大化算法[J];計算機學(xué)報;2015年02期
2 任曉龍;呂琳媛;;網(wǎng)絡(luò)重要節(jié)點排序方法綜述[J];科學(xué)通報;2014年13期
3 趙之瀅;于海;朱志良;汪小帆;;基于網(wǎng)絡(luò)社團結(jié)構(gòu)的節(jié)點傳播影響力分析[J];計算機學(xué)報;2014年04期
4 吳信東;李毅;李磊;;在線社交網(wǎng)絡(luò)影響力分析[J];計算機學(xué)報;2014年04期
5 郭小和;劉科;廖煥文;姚偉;周繼強;;基于復(fù)雜網(wǎng)絡(luò)的安然公司高管組織結(jié)構(gòu)發(fā)現(xiàn)[J];南昌航空大學(xué)學(xué)報(自然科學(xué)版);2013年03期
6 胡慶成;尹煈q，
本文編號：2262470
本文鏈接：http://sikaile.net/shoufeilunwen/benkebiyelunwen/2262470.html
最近更新
教材專著
