【摘要】：影響力最大化問題首先被Domingos和Richardson引入到社會網(wǎng)絡領域,成為社會網(wǎng)絡領域的一個熱門的研究問題。問題提出后各領域的學者紛紛開始提出各種各樣的算法用于求解社會網(wǎng)絡上的影響力最大化問題。本文針對于具有地理位置信息的商店進行影響最大化問題的研究,主要的研究內(nèi)容如下:基于喜好及位置因素的貪心算法的研究。現(xiàn)有的邊概率取值上一成不變的使用沒有現(xiàn)實意義的選取方式,本文通過提出基于喜好相似度與距離兩個因素來定義邊概率和根據(jù)距離來選取候選種子集合的方式,通過這樣的方式不僅具有現(xiàn)實的意義而且使圖中的無效節(jié)點在開始時就被排除。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,都是在整個網(wǎng)絡上去獲取種子節(jié)點,這樣會花費很大的開銷。本算法是通過對位置感知網(wǎng)絡劃分區(qū)域,對每個區(qū)域進行種子節(jié)點的選取,把各區(qū)域得到的節(jié)點集合起來得到最終的種子節(jié)點集合。本文又提出一個基于影響成功率的傳播模型,該模型是考慮到當節(jié)點影響其他節(jié)點時會根據(jù)之前成功激活節(jié)點的個數(shù)為其自身賦予一個影響成功率,影響成功率會影響之后它的鄰居節(jié)點是否被激活。最后,本文使用一部分真實數(shù)據(jù)和一部分模擬數(shù)據(jù)進行實驗驗證,并從時間和影響力兩個方面對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)域劃分算法進行驗證。
[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.
【學位授予單位】：黑龍江大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：O157.5;TP301.6

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本文編號：2353434