Better approximation algorithms for influence maximization in online social networks

Zhu, Yuqing; Wu, Weili; Bi, Yuanjun; Wu, Lidong; Jiang, Yiwei; Xu, Wen

doi:10.1007/s10878-013-9635-7

Cited by 24 publications

(5 citation statements)

References 11 publications

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“…Using approximation, the influence maximization problem has often been treated as a combinatorial optimization problem where the solutions (at the approximation step) gradually enter into near-optimal asymptotic bounds. For example, in [56], the semi-definite-based algorithm has been designed to keep the approximation ratio higher than 1-1/e if the ratio of the seeds to the total number of nodes resides in a certain range. It should be noted that, although approximation algorithms have been proposed to generate solutions that are close to near-optimal asymptotic bounds, most of these algorithms have suffered from the scalability issues.…”

Section: Related Workmentioning

confidence: 99%

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

Wang

Han

et al. 2023

IEEE Trans. Cybern.

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This paper is concerned with the influence maximization problem under a network with probabilistically unstable links (PULs) via graph embedding for multi-agent systems (MASs). First, two diffusion models, the unstable-link independent cascade (UIC) model and the unstable-link linear threshold (ULT) model, are designed for the influence maximization problem under the network with PULs. Second, the MAS model for the influence maximization problem with PULs is established and a series of interaction rules among agents are built for the MAS model. Third, the similarity of the unstable structure of the nodes is defined and a novel graph embedding method, termed the unstable-similarity2vec (US2vec) approach, is proposed to tackle the influence maximization problem under the network with PULs. According to the embedding results of the US2vec approach, the seed set is figured out by the developed algorithm. Finally, extensive experiments are conducted to 1) verify the validity of the proposed model and the developed algorithms, and 2) illustrate the optimal solution for influence maximization under different scenarios with PULs.

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Section: Related Workmentioning

confidence: 99%

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

Wang

Han

et al. 2023

IEEE Trans. Cybern.

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“…• Zohu et al's Method: Zohu et al [74] improved the approximation bound from (1 − 1 e ) (which is approximately 0.63) to 0.857. They designed two approximation algorithms: first algorithm works for the problem, where the cardinality of the seed set (S) is not restricted and the second one works, when there is some restricted upper bound on the cardinality of seed set.…”

Section: O(kmnr))mentioning

confidence: 99%

A survey on influence maximization in a social network

2020

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Given a social network with diffusion probabilities as edge weights and an integer k, which k nodes should be chosen for initial injection of information to maximize influence in the network? This problem is known as Target Set Selection in a social network (TSS Problem) and more popularly, Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain since one and half decades or so.Due to its practical importance in various domains, such as viral marketing, target advertisement, personalized recommendation, the problem has been studied in different variants, and different solution methodologies have been proposed over the years. Hence, there is a need for an organized and comprehensive review on this topic. This paper presents a survey on the progress in and around TSS Problem. At last, it discusses current research trends and future research directions as well.

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“…N Barbieri [16] proposed a topic-aware user influence propagation model by modeling authoritativeness, influence, and relevance from a topic-aware perspective. Some studies have investigated the area of maximal individual influence to find the top influential users in certain groups [17][18][19]. For influence maximization, the important works are aimed at finding a subset of influential users and then evaluating their influence using various algorithms based on an information diffusion model.…”

Section: Related Workmentioning

confidence: 99%

A Community Related Influence Evaluation Model for Social Networks with a Dynamic Perspective

Xiang

Zhang

et al. 2018

Mathematical Problems in Engineering

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The nature of free information diffusion and sharing in social networks (SNs) has increasingly attracted users to gather in communities to facilitate communication, resulting in increased challenges to community influence analysis in SNs. In SNs, the ability to evaluate how a community affects other users and communities is essential for further applications. Although considerable effort has been expended on the influence evaluation of individuals, little attention has been paid to influence evaluation of the community as a whole—particularly when considering the inherent features of dynamic changes of influence over time. This paper proposes a community related influence evaluation model with a dynamic perspective (D-CIEM) that reflects two types of influence, entity influence and community-initiated influence, by considering dynamic effects over time. Our work includes the following main points: first, we address the framework of D-CIEM and its related formal definitions; then, we propose dynamic entity influence evaluation in D-CIEM, i.e., user influence and community influence evaluation with dynamic factors. We present community-initiated dynamic influence evaluation methods, including methods to calculate community influence for internal and external users and for other communities. Finally, we provide evaluation results to verify the performance of our model.

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Better approximation algorithms for influence maximization in online social networks

Cited by 24 publications

References 11 publications

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

A survey on influence maximization in a social network

A Community Related Influence Evaluation Model for Social Networks with a Dynamic Perspective

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