Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks

Lei Yu; Guohui Li; Ling Yuan

doi:10.1145/3364993

Maximizing Boosted Influence Spread with Edge Addition in Online Social Networks

ACM/IMS Transactions on Data Science ◽

10.1145/3364993 ◽

2020 ◽

Vol 1 (2) ◽

pp. 1-21

Author(s):

Lei Yu ◽

Guohui Li ◽

Ling Yuan

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Influence Spread ◽

Edge Addition

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Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3442341 ◽

2021 ◽

Vol 15 (3) ◽

pp. 1-21

Author(s):

Guanhao Wu ◽

Xiaofeng Gao ◽

Ge Yan ◽

Guihai Chen

Keyword(s):

Social Networks ◽

Social Network ◽

Parallel Algorithms ◽

Real World ◽

Online Social Networks ◽

Heuristic Algorithms ◽

Influence Maximization ◽

Memory Overhead ◽

Influence Spread ◽

Real World Applications

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

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Pricing influential nodes in online social networks

Proceedings of the VLDB Endowment ◽

10.14778/3401960.3401961 ◽

2020 ◽

Vol 13 (10) ◽

pp. 1614-1627

Author(s):

Yuqing Zhu ◽

Jing Tang ◽

Xueyan Tang

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Potential Influence ◽

Optimal Price ◽

Privacy Concerns ◽

Marketing Campaign ◽

Influence Spread ◽

Influential Nodes ◽

Social Advertising ◽

Advertising Model

Influential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential influence spread that can be generated by these influential nodes is hidden behind the structures of OSNs, which are often held by OSN providers and unavailable to advertisers for privacy concerns. A social advertising model known as influencer marketing is to have OSN providers offer and price candidate nodes for advertisers to purchase for seeding marketing campaigns. In this setting, a reasonable price profile for the candidate nodes should effectively reflect the expected influence gain they can bring in a marketing campaign. In this paper, we study the problem of pricing the influential nodes based on their expected influence spread to help advertisers select the initiators of marketing campaigns without the knowledge of OSN structures. We design a function characterizing the divergence between the price and the expected influence of the initiator sets. We formulate the problem to minimize the divergence and derive an optimal price profile. An advanced algorithm is developed to estimate the price profile with accuracy guarantees. Experiments with real OSN datasets show that our pricing algorithm can significantly outperform other baselines.

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