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influence maximization with limit cost in social network

Science China(Information Sciences) 2013年第7期56卷 168-181页

作者： WANG Yue HUANG WeiJing ZONG Lang WANG TengJiao YANG DongQing Department of Computer Science School of Information Central University of Finance and Economics Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education

Social networking service (SNS) applications are changing the way information spreads in online communities. As real social relationships are projected into SNS applications, word of mouth has been an important factor in the information spreading processes of those applications. By assuming each user needs a cost to accept some specific information, this paper studies the initial "seed user" selection strategy to maximize information spreading in a social network with a cost budget. The main contributions of this paper are: 1) proposing a graphic SEIR model (gSEIR) by extending the epidemic compartmental model to simulate the dynamic information spreading process between individuals in the social network; 2) proposing a formal definition for the influence maximization problem with limit cost (IMLC) in social networks, and proving that this problem can be transformed to the weighted set-cover problem (WSCP) and thus is NP-Complete; 3) providing four different greedy algorithms to solve the IMLC problem; 4) proposing a heuristic algorithm based on the method of Lagrange multipliers (HILR) for the same problem; 5) providing two parts of experiments to test the proposed models and algorithms in this paper. In the first part, we verify that gSEIR can generate similar macro-behavior as an SIR model for the information spreading process in an online community by combining the micro-behaviors of all the users in that community, and that gSEIR can also simulate the dynamic change process of the statuses of all the individuals in the corresponding social networks during the information spreading process. In the second part, by applying the simulation result from gSEIR as the prediction of information spreading in the given social network, we test the effectiveness and efficiency of all provided algorithms to solve the influence maximization problem with cost limit. The result show that the heuristic algorithm HILR is the best for the IMLC problem.

关键词： data mining social network influence maximization graph-based diffusion model information propagation

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CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

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Chinese Physics B 2024年第8期33卷 588-604页

作者：董晨徐桂琼孟蕾 School of Management Shanghai UniversityShanghai 200444China

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various *** order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of *** spreading mechanism of rumors and effective suppression strategies are significant and challenging research ***,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same ***,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social *** crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed *** the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the *** results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social ***,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

关键词： online social networks rumor blocking competitive linear threshold model influence maximization

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UGGreedy: influence maximization for User Group in Microblogging

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Chinese Journal of Electronics 2016年第2期25卷 241-248页

作者： YU Miao YANG Wu WANG Wei SHEN Guowei DONG Guozhong GONG Liangyi Information Security Research Center Harbin Engineering University College of Computer Science and Technology Guizhou University

We study a new influence maximization problem about how to find a seed set which can maximize the influence spread to a targeted user group in microblogging. To solve this problem, we propose a threestage User group greedy algorithm(UGGreedy) based on user attributes. To reduce network scale, we delete useless user nodes, and rank the rest of users based on user attributes to form a seed candidate set. We employ the seed candidate set to construct a simplified microblogging network graph. We propose a novel influence greedy algorithm based on influence accumulation spread to find the seed *** results show that UGGreedy can achieve remarkable efficiency on the influence maximization problem for user group in real microblogging networks.

关键词： Microblogging User attribute User group influence maximization

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Approximating Special Social influence maximization Problems

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Tsinghua Science and Technology 2020年第6期25卷 703-711页

作者： Jie Wu Ning Wang the Department of Computer and Information Sciences Temple University.PhiladelphiaPA 19122USA the Department of Computer Science Rowan UniversityGlassboroNJ 08028USA

Social influence maximization Problems(SIMPs)deal with selecting k seeds in a given Online Social Network(OSN)to maximize the number of eventually-influenced *** is done by using these seeds based on a given set of influence probabilities among neighbors in the *** the SIMP has been proved to be NP-hard,it has both submodular(with a natural diminishing-return)and monotone(with an increasing influenced users through propagation)that make the problem suitable for approximation ***,several special SIMPs cannot be modeled as submodular or monotone *** this paper,we look at several conditions under which non-submodular or non-monotone functions can be handled or *** is a profit-maximization SIMP where seed selection cost is included in the overall utility function,breaking the monotone *** other is a crowd-influence SIMP where crowd influence exists in addition to individual influence,breaking the submodular *** then review several new techniques and notions,including double-greedy algorithms and the supermodular degree,that can be used to address special *** main results show that for a specific SIMP model,special network structures of OSNs can help reduce its time complexity of the SIMP.

关键词： influence maximization online social networks submodular function

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An influence maximization Algorithm Based on the Mixed Importance of Nodes

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Computers, Materials & Continua 2019年第5期59卷 517-531页

作者： Yong Hua Bolun Chen Yan Yuan Guochang Zhu Jialin Ma HuaiYin Institute of Technology Huaian223003China University of Fribourg Fribourg1700Switzerland

The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social ***,the comprehensive influence of node needs to be considered,when we choose the most influential node set consisted of k seed *** account of the traditional methods used to measure the influence of nodes,such as degree centrality,betweenness centrality and closeness centrality,consider only a single aspect of the influence of node,so the influence measured by traditional methods mentioned above of node is not *** this paper,we obtain the following result through experimental analysis:the influence of a node is relevant not only to its degree and coreness,but also to the degree and coreness of the n-order neighbor ***,we propose a algorithm based on the mixed importance of nodes to measure the comprehensive influence of node,and the algorithm we proposed is simple and *** addition,the performance of the algorithm we proposed is better than that of traditional influence maximization algorithms.

关键词： influence maximization social network mixed importance coreness

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An influence maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

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Computers, Materials & Continua 2023年第5期75卷 3111-3131页

作者： Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui College of Computer and Control Engineering of Qiqihar University Qiqihar161006China College of Teacher Education of Qiqihar University Qiqihar161006China

influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely *** solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social ***,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell ***,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of ***,the node with the highest comprehensive degree in each core layer is selected as the ***,the seed selection strategy of KT can easily lose some influential ***,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed *** then searches for seeds in the candidate seed set according to the comprehensive *** showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than ***,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.

关键词： Temporal social network influence maximization improved K-shell comprehensive degree

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A Novel influence maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics

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Big Data Mining and Analytics 2022年第2期5卷 130-139页

作者： Jie Tong Leilei Shi Lu Liu John Panneerselvam Zixuan Han the School of Computer Science and Telecommunication Engineering and Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu UniversityZhenjiang 212013China the School of Computing and Mathematical Sciences University of LeicesterLeicester LE17RHUK

Online social networks are increasingly connecting people around the *** maximization is a key area of research in online social networks,which identifies influential users during information *** of the existing influence maximization methods only consider the transmission of a single channel,but real-world networks mostly include multiple channels of information transmission with competitive *** problem of influence maximization in an environment involves selecting the seed node set for certain competitive information,so that it can avoid the influence of other information,and ultimately affect the largest set of nodes in the *** this paper,the influence calculation of nodes is achieved according to the local community discovery algorithm,which is based on community dispersion and the characteristics of dynamic community ***,considering two various competitive information dissemination cases as an example,a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known,and a novel influence maximization algorithm of node avoidance based on user interest is *** conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.

关键词： influence maximization competitive environment dynamic network

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An influence maximization Algorithm Based on the influence Propagation Range of Nodes

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Journal on Internet of Things 2019年第2期1卷 77-88页

作者： Yong Hua Bolun Chen Yan Yuan Guochang Zhu Fenfen Li HuaiYin Institute of Technology Huaian223003China University of Fribourg Fribourg1700Switzerland

The problem of influence maximization in the social network G is to find k seed nodes with the maximum *** seed set S has a wider range of influence in the social network G than other same-size node *** influence of a node is usually established by using the IC model(Independent Cascade model)with a considerable amount of Monte Carlo simulations used to approximate the influence of the *** addition,an approximate effect(1􀀀1=e)is obtained,when the number of Monte Carlo simulations is 10000 and the probability of propagation is very *** this paper,we analyze that the propagative range of influence of node set is limited in the IC model,and we find that the influence of node only spread to the t0-th ***,we propose a greedy algorithm based on the improved IC model that we only consider the influence in the t0-th neighbor of ***,we perform experiments on 10 real social network and achieve favorable results.

关键词： influence maximization social network IC range

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An Efficient Algorithm for influence maximization under Linear Threshold Model

An Efficient Algorithm for Influence Maximization under Line...

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第26届中国控制与决策会议

作者： Shengfu Zhou Kun Yue Qiyu Fang Yunlei Zhu Weiyi Liu Department of Computer Science and Engineering School of Information Science and EngineeringYunnan University

influence maximization is to find a small set of most influential nodes in the social networks to maximize their aggregated influence in the network. The high complexity of the classical greedy algorithm cannot be well suited for the moderate or large scale networks. It is necessary to develop a more efficient algorithm, not sensitive to the scale of the social network. In this paper, we propose an approach for estimating the nodes’ influence based on the network structure. By this way, we make the scope of influence reduced to the nodes with the maximal influence, while make the consuming time reduced consequently. Then, we design a more efficient greedy algorithm（called LNG algorithm） for the linear threshold model. Experimental results on large scale networks demonstrate that the time consuming is much less and the influence spread effect is better than the classical greedy algorithm.

关键词： Social networks influence maximization Linear Threshold Model Greedy algorithm

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Budget Allocation for Maximizing Viral Advertising in Social Networks

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Journal of Computer Science & Technology 2016年第4期31卷 759-775页

作者： Bo—LeiZhang Zhu-Zhong Qian Wen-Zhong Li Bin Tang Sang-Lu Lu Xiaoming Fu State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Institute of Computer Science University of G5ttingen G5ttingen 37077 Germany

Viral advertising in social networks has arisen as one of the most promising ways to increase brand awareness and product sales. By distributing a limited budget, we can incentivize a set of users as initial adopters so that the advertising can start from the initial adopters and spread via sociM links to become viral. Despite extensive researches in how to target the most influential users, a key issue is often neglected： how to incentivize the initial adopters. In the problem of influence maximization, the assumption is that each user has a fixed cost for being initial adopters, while in practice, user decisions for accepting the budget to be initial adopters are often probabilistic rather than deterministic. In this paper, we study optimal budget allocation in social networks to maximize the spread of viral advertising. In particular, a concave probability model is introduced to characterize each user＇s utility for being an initial adopter. Under this model, we show that it is NP-hard to find an optimal budget allocation for maximizing the spread of viral advertising. We then present a novel discrete greedy algorithm with near optimal performance, and further propose scaling-up techniques to improve the time-efficiency of our algorithm. Extensive experiments on real-world social graphs are implemented to validate the effectiveness of our algorithm in practice. The results show that our algorithm can outperform other intuitive heuristics significantly in almost all cases.

关键词： social network influence maximization information diffusion submodular optimization

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