【摘要】：近年來,隨著軟件與硬件的飛速發(fā)展以及個(gè)人電腦和互聯(lián)網(wǎng)的普及,基于熟人關(guān)系的網(wǎng)絡(luò)如微信、基于同學(xué)關(guān)系的網(wǎng)絡(luò)如人人網(wǎng)和基于關(guān)注關(guān)系的網(wǎng)絡(luò)如微博等各類在線社交平臺(tái)深受人們的喜愛并占據(jù)著人們幾乎所有的業(yè)余時(shí)間,這些平臺(tái)可以產(chǎn)生海量的數(shù)據(jù),給社會(huì)網(wǎng)絡(luò)分析帶來了前所未有的機(jī)會(huì),因此吸引了大批科研工作者對社會(huì)網(wǎng)絡(luò)空間結(jié)構(gòu)、傳播規(guī)律等課題的研究和分析。其中,如何選擇社會(huì)網(wǎng)絡(luò)里影響力最大化的TOP-K節(jié)點(diǎn)及如何挑選社會(huì)網(wǎng)絡(luò)傳播模型這兩個(gè)方向,成為了學(xué)術(shù)界研究的熱門選擇。本文首先在前人研究的基礎(chǔ)上,對社會(huì)網(wǎng)絡(luò)影響力最大化算法里現(xiàn)有的算法進(jìn)行了改進(jìn);其次,詳細(xì)分析了獨(dú)立級聯(lián)模型和線性閾值模型,并引入人們在第一次接收信息和以后再次接收信息時(shí)會(huì)有不同反應(yīng)這一現(xiàn)象以及遺忘規(guī)律,提出了一種新型的社會(huì)網(wǎng)絡(luò)傳播模型。具體研究內(nèi)容如下:(1)基于三度影響力原則的線性衰減度中心性算法。根據(jù)三度影響力原則,影響力主要在三度分隔以內(nèi)有效,超過三度分隔,影響力幾乎趨近于0。因此線性衰減度中心性以節(jié)點(diǎn)在三度分隔以內(nèi)的潛在影響力來衡量節(jié)點(diǎn)的實(shí)際影響力,且這種潛在影響力從源節(jié)點(diǎn)向外傳播到距離為2時(shí)影響力衰減到原來的α倍,傳播到距離為3時(shí)再次衰減β倍,其中0α,β1。計(jì)算出線性衰減度中心性之后,本文從3種不同的角度分別在4個(gè)公共數(shù)據(jù)集上驗(yàn)證了算法的有效性。(2)混合式傳播模型。真實(shí)的人際關(guān)系網(wǎng)絡(luò)里存在著如下的事實(shí):人們在第一次接觸某些信息時(shí),是否接受常常取決于信息本身;而在第一次拒絕之后,以后的每一次是否接受取決于以往所拒絕的人和現(xiàn)在推薦的人對其影響力的累積是否大于其自身的閾值,且累積的影響力遵循著遺忘規(guī)律會(huì)隨著時(shí)間的推進(jìn)而不斷衰減�；旌鲜絺鞑ツＰ蛧L試基于這些事實(shí),吸收獨(dú)立級聯(lián)模型和線性閾值模型的精華,新提出一種更加符合社會(huì)網(wǎng)絡(luò)影響力傳播規(guī)律的傳播模型,并以兩種不同的驗(yàn)證方法在維基百科投票數(shù)據(jù)集上驗(yàn)證了混合式傳播模型的有效性。
[Abstract]:In recent years, with the rapid development of software and hardware and the popularity of personal computers and the Internet, networks based on acquaintance relationships such as WeChat, Various online social platforms, such as Renren based on classmate relationship and Weibo based on concern, are popular and occupy almost all of our spare time. These platforms can generate huge amounts of data. It brings an unprecedented opportunity to the analysis of social network, so it attracts a large number of researchers to study and analyze the spatial structure of social network, the law of communication and so on. Among them, how to choose the TOP-K node with maximum influence in social network and how to select the communication model of social network have become the hot choice of academic research. In this paper, based on the previous studies, the existing algorithms of maximizing the influence of social networks are improved. Secondly, the independent cascade model and the linear threshold model are analyzed in detail. By introducing the phenomenon that people will react differently when they receive information for the first time and then receive the information again, and the law of forgetting, a new social network communication model is proposed. The main contents are as follows: (1) A linear attenuation centrality algorithm based on the three-degree influence principle. According to the principle of three degrees of influence, influence is mainly effective within three degrees of separation, more than three degrees of separation, and the influence is almost close to 0. Therefore, linear attenuation centrality measures the actual influence of nodes by the potential influence of nodes within three degrees, and this potential influence propagates from the source node outward to the distance of 2. The propagation to the distance of 3 again attenuates 尾 times, in which 0 偽, 尾 1. After calculating the centrality of linear attenuation, this paper verifies the validity of the algorithm on four common data sets from three different angles. (2) Hybrid propagation model. In a real network of relationships, there is the fact that when people first come into contact with certain information, it often depends on the information itself, and after the first rejection, The acceptance of each time in the future depends on whether the cumulative influence of the person who has been rejected or recommended now is greater than its own threshold and that the cumulative influence follows the law of forgetting will continue to decline with the advance of time. Based on these facts and absorbing the essence of the independent cascade model and the linear threshold model, a new communication model is proposed, which is more consistent with the law of social network influence transmission. Two different verification methods are used to verify the validity of the hybrid propagation model on the Wikipedia voting data set.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：G206;TP301.6

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