On Influence Maximization to Target Users in the Presence of Multiple Acceptances

Chang, Chien-Wei; Yeh, Mi-Yen; Chuang, Kun-Ta

doi:10.1145/2808797.2809349

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…TC-CIM problem can be proved as NP-hard by transforming it into a single party time-constrained influence maximization that has already been proved as an NP-hard [21], [22], [39]. To find an optimal solution of TC-CIM problem is impractical as it is NP-hard.…”

Section: Methodsmentioning

confidence: 99%

A Novel Nested Q-Learning Method to Tackle Time-Constrained Competitive Influence Maximization

2019

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Time plays a critical role in competitive influence maximization. Companies aim to promote their products before certain events, such as Christmas Eve or music concerts, to gain more benefit under competitions from other companies. Besides, these companies have a limited budget to spend on these product promotions. Therefore, in this paper, we examine a time-constrained competitive influence maximization where the parties wish to maximize their profits before the respective deadlines. Besides, the parties need to determine how to select the seed nodes and when to initiate information propagation in the network, such that the decision results in the optimal reward given the time and the budget constraint. To this end, we propose a novel reinforcement learning-based framework named seed-combination and seed-selection that is built on a nested Q-learning (NSQ) algorithm. This way, we can derive the optimal in both budget allocation and node selection that results in the maximum profit. In evaluating the proposed model, we consider the scenarios when the competitors' strategy is known, unknown, and not available for training. The results show that the proposed NSQ algorithm could improve the rewards by up to 50% compared with the state-of-the-art algorithm, STORM-Q.

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Section: Methodsmentioning

confidence: 99%

A Novel Nested Q-Learning Method to Tackle Time-Constrained Competitive Influence Maximization

2019

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“…Variations of the problem of target influence propagation has been studied recently. Chang et al [4] introduce a related formulation in a context where users can be activated multiple times. Lee et al cast the problem of maximizing influence over certain users as a query processing problem [19].…”

Section: Related Workmentioning

confidence: 99%

“…. , Cr} be the partition of G into its strongly connected components (SCC) 4 . Also, for each node u ∈ [n] we denote the strongly connected component that includes u by C(u).…”

Section: Directed Graphsmentioning

confidence: 99%

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Real-Time Targeted-Influence Queries over Large Graphs

Epasto

Mahmoody

Upfal

2017

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

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Social networks are important communication and information media. Individuals in a social network share information and influence each other through their social connections. Understanding social influence and information diffusion is a fundamental research endeavor and it has important applications in online social advertising and viral marketing. In this work, we introduce the Targeted-Influence problem (TIP): Given a network G = (V, E) and a model of influence, we want to be able to estimate in real-time (e.g. a few seconds per query) the influence of a subset of users S over another subset of users T , for any possible query (S; T), S, T ⊆ V. To do so, we allow an efficient preprocessing. We provide the first scalable real-time algorithm for TIP. Our algorithm requiresÕ(|V| + |E|) space and preprocessing time, and it provides a provable approximation of the influence of S over T , for every subsets of nodes S, T ⊆ V in the query with large enough influence. The running time for answering each query (a.k.a query stage) is theoretically guaranteed to beÕ(|S| + |T |) in general undirected and for directed graphs under certain assumptions, supported by experiments. We also introduce the Snapshot model as our model of influence, which extends and includes as special case both the Independent Cascade and the Linear Threshold models. The analysis and the theoretical guarantees of our algorithms hold under the more general Snapshot model. Finally, we perform an extensive experimental analysis, demonstrating the accuracy, efficiency, and scalability of our methods.

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“…The study of influence towards target users is becoming more important due to its essential applicability to viral marketing [2], [34]. One of the pioneering studies toward influence maximisation towards target user(s) was carried out by [24], where they proposed the Local Influence Maximization problem (LIMP).…”

Section: B Information Diffusion and The Influencementioning

confidence: 99%

Influence Maximization Towards Target Users on Social Networks for Information Diffusion

Olanrewaju

Ahmad

Mahmudin

2017

Recent Trends in Information and Communication Technology

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Influence maximisation has been an area of active research in recent years. This study aims to extend the fundamental influence maximisation problem (IMP) with respect to a set of target users on a social network. It is important to aim at the target users to speed up the rate of information diffusion and reduce the information diffusion cost. In doing so, the MITU algorithm was formulated and compared with state of the art algorithms. Publicly available datasets were used in validating the proposed algorithm. It was found that the MITU identified all target nodes while significantly lowering the information diffusion cost function (IDCF) by up to 79%. The influence overlap problem was equally identified in the heuristic algorithm where the seed set size was reduced by an average of six times. Furthermore, the random influencer selection identifies target nodes better than the betweenness and PageRank centralities. The findings could help organisations to reach target users on social media in the shortest cycle.

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On Influence Maximization to Target Users in the Presence of Multiple Acceptances

Cited by 9 publications

References 6 publications

A Novel Nested Q-Learning Method to Tackle Time-Constrained Competitive Influence Maximization

A Novel Nested Q-Learning Method to Tackle Time-Constrained Competitive Influence Maximization

Real-Time Targeted-Influence Queries over Large Graphs

Influence Maximization Towards Target Users on Social Networks for Information Diffusion

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