一种利用复合事件概率运算解决负信息抑制最大化问题的方法  

A Method of Using Compound Event Probability Operation to Solve Problem of Negative Information Blocking Maximization

作　　者：王晓航 李永杰[1] 余雷 范萧 WANG Xiaohang;LI Yongjie;YU Lei;FAN Xiao(School of Electronic Engineering,Naval University of Engineering,Wuhan 430033,China;Unit 95174 of PLA,Wuhan 430068,China)

机构地区：[1]海军工程大学电子工程学院,湖北武汉430033 [2]中国人民解放军95174部队,湖北武汉430048

出　　处：《计算机与现代化》2024年第12期24-33,共10页Computer and Modernization

基　　金：国家自然科学基金资助项目(62276110)。

摘　　要：在线社交网络为人们提供通信便利的同时,也会广泛传播负面信息从而造成严重的负面影响。因此,亟需采取合理有效的策略来最大程度地抑制网络中负面信息的传播。本文在COICM模型上研究了负信息抑制最大化问题并基于局部影响入树结构设计求解节点正(负)激活概率的方法,进而提出一种启发式算法来求解此问题。核心思想为首先在局部影响入树中区分节点的状态,即节点在当前时刻被正或(负)激活,在当前时刻之前曾被正(负)激活和直到当前时刻保持不被激活,且5种状态组成该节点截至当前时刻发生事件的样本空间,然后利用复合事件概率的运算方法求出节点在当前时刻的被正(负)激活的概率表达式,以及通过递归计算求出根节点的负激活概率,最后将网络中所有节点的负激活概率之和作为负种子集的影响力。该算法使用贪心框架迭代选择负信息抑制最大的节点作为传播正信息的节点。在4种真实的不同规模的社交网络数据集上与现有算法进行对比分析,结果表明本文算法的负信息抑制效果更好,且能够适用于大规模网络。While online social networks provide people with convenient information interaction,they also widely spread negative information thus cause panic in the society.Therefore,it is urgent to take reasonable and effective strategies to block the spread of negative information in the network to the greatest extent.In COICM model,this paper studies the problem of negative informa-tion blocking maximization and designs a method to compute the positive(negative)activation probabilities of nodes based on maximum influence in-arborescence,and then proposes a heuristic algorithm to solve this problem.The core idea is that,firstly,distinguishing the state of nodes in the local impact in-tree,that is,the node is positive(negative)activated at the current time,has been positive or negative activated before the current time and remains inactive until the current time,and the five states con-stitute the sample space of the events occurring at the node up to the current time.Then use the compound event probability op-eration method to work out the probability expression of positive(negative)activation of the node at the current time as well as calculate the negative activation probability of the root node through recursive calculation.Finally,take the sum of the negative activation probabilities of all nodes in the network as the influence of the negative seed set.The algorithm uses the greedy frame-work to iteratively select the node with the largest negative information blocking as the node to propagate positive information.Compared with existing algorithms on four real social network datasets of different sizes,the results show that the proposed algo-rithm has better negative information blocking effect,and can be applied to large-scale networks.

关 键 词：在线社交网络 影响力抑制最大化 启发式算法 多信息 竞争独立级联模型 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]