【摘要】：影響力最大化問(wèn)題首先被Domingos和Richardson引入到社會(huì)網(wǎng)絡(luò)領(lǐng)域,成為社會(huì)網(wǎng)絡(luò)領(lǐng)域的一個(gè)熱門(mén)的研究問(wèn)題。問(wèn)題提出后各領(lǐng)域的學(xué)者紛紛開(kāi)始提出各種各樣的算法用于求解社會(huì)網(wǎng)絡(luò)上的影響力最大化問(wèn)題。本文針對(duì)于具有地理位置信息的商店進(jìn)行影響最大化問(wèn)題的研究,主要的研究?jī)?nèi)容如下:基于喜好及位置因素的貪心算法的研究�，F(xiàn)有的邊概率取值上一成不變的使用沒(méi)有現(xiàn)實(shí)意義的選取方式,本文通過(guò)提出基于喜好相似度與距離兩個(gè)因素來(lái)定義邊概率和根據(jù)距離來(lái)選取候選種子集合的方式,通過(guò)這樣的方式不僅具有現(xiàn)實(shí)的意義而且使圖中的無(wú)效節(jié)點(diǎn)在開(kāi)始時(shí)就被排除。提出基于影響成功模型的區(qū)域劃分算法,在以往的影響最大化算法中,都是在整個(gè)網(wǎng)絡(luò)上去獲取種子節(jié)點(diǎn),這樣會(huì)花費(fèi)很大的開(kāi)銷(xiāo)。本算法是通過(guò)對(duì)位置感知網(wǎng)絡(luò)劃分區(qū)域,對(duì)每個(gè)區(qū)域進(jìn)行種子節(jié)點(diǎn)的選取,把各區(qū)域得到的節(jié)點(diǎn)集合起來(lái)得到最終的種子節(jié)點(diǎn)集合。本文又提出一個(gè)基于影響成功率的傳播模型,該模型是考慮到當(dāng)節(jié)點(diǎn)影響其他節(jié)點(diǎn)時(shí)會(huì)根據(jù)之前成功激活節(jié)點(diǎn)的個(gè)數(shù)為其自身賦予一個(gè)影響成功率,影響成功率會(huì)影響之后它的鄰居節(jié)點(diǎn)是否被激活。最后,本文使用一部分真實(shí)數(shù)據(jù)和一部分模擬數(shù)據(jù)進(jìn)行實(shí)驗(yàn)驗(yàn)證,并從時(shí)間和影響力兩個(gè)方面對(duì)基于喜好及位置因素的貪心算法和基于影響成功率模型的區(qū)域劃分算法進(jìn)行驗(yàn)證。
[Abstract]:The problem of maximization of influence is first introduced into the field of social network by Domingos and Richardson, which has become a hot research problem in the field of social network. Since the problem was put forward, scholars in various fields have begun to put forward a variety of algorithms to solve the problem of maximization of influence on social networks. The main contents of this paper are as follows: greedy algorithm based on preferences and location factors. In this paper, we propose a method to define edge probability based on preference similarity and distance and to select candidate seed set according to distance. In this way, not only does it have practical significance, but also the invalid nodes in the graph are excluded from the beginning. A region partition algorithm based on the influence success model is proposed. In the previous impact maximization algorithm, the seed nodes are obtained on the whole network, which will cost a lot of money. The algorithm divides the region of the location-aware network and selects the seed nodes for each region, and then gathers the nodes from each region to get the final seed node set. In this paper, a propagation model based on influence success rate is proposed. This model considers that when nodes affect other nodes, they are given a success rate according to the number of previously successfully activated nodes. The success rate affects whether its neighbor node is activated or not. Finally, some real data and some simulated data are used to verify the experiment. The greedy algorithm based on preference and location factors and the region partition algorithm based on influence success rate model are verified from two aspects of time and influence.
【學(xué)位授予單位】：黑龍江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：O157.5;TP301.6

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本文編號(hào)：2353434