Social Influence Maximization in Hypergraph in Social Networks

Zhu, Jianming; Zhu, Junlei; Ghosh, Smita; Wu, Weili; Yuan, Jing

doi:10.1109/tnse.2018.2873759

Cited by 79 publications

(38 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Corley & Sha [7] address the problem of n-most vital nodes problem and propose the algorithm to solve the problem of node importance evaluation. Currently, many efforts have been made to discover the most influential nodes for maximizing influence in social networks [8]- [10]. These studies of influence maximization aim to discover nodes that can activate as many nodes as possible, which indicates that the influence of nodes can be propagated as extensively as possible.…”

Section: Structural Methodsmentioning

confidence: 99%

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

Mao

Zhou

Xiong

2021

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

Section: Structural Methodsmentioning

confidence: 99%

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

Mao

Zhou

Xiong

2021

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

“…Following Kempe's work, [5][6][7][8][9][10][11][12] have studied different types of IM problems. Realizing the exist of crowd influence, IM problem with considering crowd influence was studied by Zhu [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

“…Nguyen et al [23] made a breakthrough and proposed Dynamic-Stop-and-Stare Algorithm (D-SSA) which was much faster while guarantee the same approximation ratio. Zhu [13] presented weighted RIS sampling method.…”

Section: Related Workmentioning

confidence: 99%

Non-submodular model for group profit maximization problem in social networks

Zhu

Ghosh

et al. 2021

Comput Soc Netw

Self Cite

View full text Add to dashboard Cite

In social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that most of the users in this group reach an agreement, such as US Presidential Elections. A group is called activated if $$\beta$$ β percent of users are influenced in the group. Enterprise will gain income from all influenced groups. Simultaneously, to propagate influence, enterprise needs pay advertisement diffusion cost. Group profit maximization (GPM) problem aims to pick k seeds to maximize the expected profit that considers the benefit of influenced groups with the diffusion cost. GPM is proved to be NP-hard and the objective function is proved to be neither submodular nor supermodular. An upper bound and a lower bound which are difference of two submodular functions are designed. We propose a submodular–modular algorithm (SMA) to solve the difference of two submodular functions and SMA is shown to converge to a local optimal. We present an randomized algorithm based on weighted group coverage maximization for GPM and apply sandwich framework to get theoretical results. Our experiments verify the efficiency of our methods.

show abstract

“…Diffusion Based on Obfuscated Coded Data; TNSE Oct.-Dec. 2019 968-982 Wang, J., see Wang, J., TNSE Oct.-Dec. 2019 968-982 Wang, L., and Cheng, S., Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach; TNSE July-Sept. 2019 267-279 Wang, X., Duan, L., and Zhang, J., Mobile Social Services with Network Externality: From Separate Pricing to Bundled Pricing; TNSE July-Sept. 2019 379-390 Wang, X., He, J., Cheng, P., and Chen, J., Differentially Private Maximum Consensus: Design, Analysis and Impossibility Result; TNSE Oct.-Dec. 2019 928-939 Wang, X., see Wu, H., TNSE Oct.-Dec. 2019 646-656 Wang, X., see Zhang, J., TNSE Oct.-Dec. 2019 952-967 Wu, B., Shen, H., andChen, K., SPread: Exploiting Fractal Wu, T., Chang, C., and Liao, W., Tracking Network Evolution and Their Appli-cations in Structural Network Analysis; TNSE July-Sept. 2019 562-575 Wu, W., see Zhu, J., TNSE Oct.-Dec. 2019 801-811 X Xiao, Y., Dorfler, F., Schaar, M.v.d., Incentive Design in Peer Review: Rating and Repeated Endogenous Matching;898-908 Xu, M., see Wang, J., TNSE Jan.-March 2019 Y Yannakakis, M., see Soltan, S., TNSE July-Sept. 2019459-473 Yao, Y., see Zhang, J., TNSE Oct.-Dec. 2019 Yatauro, M., The Edge Cover Probability Polynomial of a Graph and Optimal Network Construction; TNSE July-Sept. 2019 538-547 Yeh, E., see Zhang, J., TNSE July-Sept. 2019474-487 Ying, L., see Liu, X., TNSE Oct.-Dec. 2019909-916 Yousefizadeh, H., see Gharib, M., TNSE July-Sept. 2019501-511 Yu, L., see Qiu, C., TNSE July-Sept. 2019 Yu, W., see Hong, H., TNSE Oct.-Dec. 2019760-772 Yu, X., see Hamedmoghadam, H., TNSE July-Sept. 2019 Yu, X., see Hong, H., TNSE Oct.-Dec. 2019 760-772 Yuan, J., see Zhu, J., TNSE Oct.-Dec. 2019801-811 Z Zelazo, D., see Mukherjee, D., TNSE Oct.-Dec. 2019 Zhan, C., and Tse, C.K., A Model for Growth of Markets of Products or Services Having Hierarchical Dependence; TNSE July-Sept. 2019 198-209 Zhang, H., see Du, J., TNSE April -June 2019 103-115 Zhang, J., see Wang, X., TNSE July-Sept. 2019 379-390 Zhang, J., Yeh, E., and Modiano, E., Robustness of Interdependent Random Geometric Networks; TNSE July-Sept. 2019 474-487 Zhang, J., Fu, L., Li, S., Yao, Y., and Wang, X., Core Percolation in Interdependent Networks; TNSE Oct.-Dec. 2019 952-967 Zhang, L., see Wang, B., TNSE July-Sept. 2019 523-537 Zhang, Y., and Cortes, J., Characterizing Tolerable Disturbances for Transient-State Safety in Power Networks; TNSE July-Sept. 2019 210-224 Zhang, Z., see Qi, Y., TNSE July-Sept...…”

Section: Enhancing the Anonymity In Informationmentioning

confidence: 99%

2019 Index IEEE Transactions on Network Science and Engineering Vol. 6

2019

IEEE Trans. Netw. Sci. Eng.

View full text Add to dashboard Cite

This index covers all technical items-papers, correspondence, reviews, etc.-that appeared in this periodical during 2019, and items from previous years that were commented upon or corrected in 2019. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.

show abstract

Social Influence Maximization in Hypergraph in Social Networks

Cited by 79 publications

References 15 publications

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

Non-submodular model for group profit maximization problem in social networks

2019 Index IEEE Transactions on Network Science and Engineering Vol. 6

Contact Info

Product

Resources

About