基于社交网络的趋势预测
发布时间：2018-11-02 20:28
【摘要】：近年来,社交网络由于其便利性和及时性,成为人们分享和交流的一个主要平台,也带来了在线媒体信息的爆炸性增长。挖掘在线媒体中热点信息成为一个备受关注的研究方向,其中预测在线社交网络中内容的流行趋势对营销、流量控制都具有重要意义。本文分析了国内外社交网络流行趋势的研究现状,通过分析社交网络中社交关系和社会影响,提出了预测流行趋势和用户行为的解决方案。本文主要工作如下:1.本文通过对典型社交网站数据的分析,发现内容发布后,转发过程中不同时间段的转发用户对内容流行度有重要作用,并且发现活跃度高但是相互关注数不高的用户对其朋友的影响更大;部分内容是潜在流行内容,他们在前期不流行,但是随着时间推移反而变得十分流行。基于这些发现,本文提出一种发现社交网络内容传播过程中的关键节点的方法,用于预测潜在流行内容和用户转发行为。2.本文根据社交网络内容转发序列中用户的关键性提出了一种基于关键节点来预测内容流行趋势的算法框架。基于转发用户的流行预测算法首先将内容转发序列划分成T个时间窗,然后提取每个时间片段内转发用户的关键性作为T维特征,并用回归算法对最终流行度进行预测。在典型社交网络(微博)数据集上进行实验,发现相比现有流行预测算法,本文提出的算法在预测准确度和排序准确度上都有明显提升(MAE提升36.8%,tau提升2.9%),并且基于转发用户的趋势预测方法在预测流行度较高的内容更加准确,也能更早地发现流行内容。3.本文分析社交网络中的社会影响,用局部子网络来描述全局网络对用户的社会影响,提出根据社会影响和节点关键性预测用户行为的模型。基于局部社会影响的趋势预测方法首先构建目标用户的局部子网络,然后根据邻居节点关键性和节点之间相关性衡量局部网络的社会影响。在典型社交网络(微博)数据集上进行实验,对比现有预测用户转发行为的算法,发现分类效果有明显提升(相比基准算法分类准确率提升20.6%)。这说明局部网络的社会影响确实存在并影响了用户的行为。本文还研究了局部网络影响聚合的不同方法,发现社会影响是与时间密切相关的,随着邻居节点转发时间越久,其累积的影响也越大。
[Abstract]:In recent years, because of its convenience and timeliness, social network has become a main platform for people to share and communicate, and has also brought the explosive growth of online media information. Mining hot information in online media has become a hot research direction, in which predicting the popular trend of online social network content is of great significance to marketing and traffic control. This paper analyzes the current situation of social networking trends at home and abroad, and puts forward solutions to predict popular trends and user behaviors by analyzing social relations and social impacts in social networks. The main work of this paper is as follows: 1. By analyzing the data of typical social networking sites, we find that after the content is released, the forwarding users in different time periods play an important role in the popularity of the content. It was also found that users with high activity but low mutual attention had greater influence on their friends. Part of the content is potential popular content, they were not popular in the early days, but became very popular over time. Based on these findings, this paper proposes a method to discover key nodes in the process of content propagation on social networks, which is used to predict potential popular content and user forwarding behavior. 2. This paper presents an algorithm framework to predict the trend of social network content based on key nodes according to the key of users in the social network content forwarding sequence. The popular prediction algorithm based on forwarding users first divides the content forwarding sequence into T time windows, then extracts the key of forwarding users within each time segment as T-dimension feature, and predicts the final popularity with regression algorithm. Experiments on typical social network (Weibo) data set show that the proposed algorithm has a significant improvement in prediction accuracy and sorting accuracy (MAE increase is 36.8% and tau increase 2.9%). And the trend forecasting method based on forwarding users is more accurate in predicting the content with higher popularity, and can also find the popular content earlier. This paper analyzes the social impact in social networks, describes the social impact of global networks on users by using local subnetworks, and puts forward a model to predict user behavior based on social impact and node key. The trend prediction method based on the local social impact firstly constructs the local subnetwork of the target user and then measures the social impact of the local network according to the key of the neighbor node and the correlation between the nodes. Experiments on typical social network (Weibo) data sets show that the classification effect is significantly improved (20.6% higher than the classification accuracy of the benchmark algorithm) by comparing the existing algorithms for predicting user forwarding behavior. This shows that the social impact of local networks does exist and affect the behavior of users. This paper also studies different methods of local network influencing aggregation. It is found that social impact is closely related to time. The longer the neighbor node forwards, the greater the cumulative impact is.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP393.09
本文编号：2306864
[Abstract]:In recent years, because of its convenience and timeliness, social network has become a main platform for people to share and communicate, and has also brought the explosive growth of online media information. Mining hot information in online media has become a hot research direction, in which predicting the popular trend of online social network content is of great significance to marketing and traffic control. This paper analyzes the current situation of social networking trends at home and abroad, and puts forward solutions to predict popular trends and user behaviors by analyzing social relations and social impacts in social networks. The main work of this paper is as follows: 1. By analyzing the data of typical social networking sites, we find that after the content is released, the forwarding users in different time periods play an important role in the popularity of the content. It was also found that users with high activity but low mutual attention had greater influence on their friends. Part of the content is potential popular content, they were not popular in the early days, but became very popular over time. Based on these findings, this paper proposes a method to discover key nodes in the process of content propagation on social networks, which is used to predict potential popular content and user forwarding behavior. 2. This paper presents an algorithm framework to predict the trend of social network content based on key nodes according to the key of users in the social network content forwarding sequence. The popular prediction algorithm based on forwarding users first divides the content forwarding sequence into T time windows, then extracts the key of forwarding users within each time segment as T-dimension feature, and predicts the final popularity with regression algorithm. Experiments on typical social network (Weibo) data set show that the proposed algorithm has a significant improvement in prediction accuracy and sorting accuracy (MAE increase is 36.8% and tau increase 2.9%). And the trend forecasting method based on forwarding users is more accurate in predicting the content with higher popularity, and can also find the popular content earlier. This paper analyzes the social impact in social networks, describes the social impact of global networks on users by using local subnetworks, and puts forward a model to predict user behavior based on social impact and node key. The trend prediction method based on the local social impact firstly constructs the local subnetwork of the target user and then measures the social impact of the local network according to the key of the neighbor node and the correlation between the nodes. Experiments on typical social network (Weibo) data sets show that the classification effect is significantly improved (20.6% higher than the classification accuracy of the benchmark algorithm) by comparing the existing algorithms for predicting user forwarding behavior. This shows that the social impact of local networks does exist and affect the behavior of users. This paper also studies different methods of local network influencing aggregation. It is found that social impact is closely related to time. The longer the neighbor node forwards, the greater the cumulative impact is.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP393.09
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，本文编号：2306864
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