• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1263-1274
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• 2021

In online competitive social networks, each user can be influenced by different competing influencers and consequently chooses different products. But their interest may change over time and may have swings between different products. The existing influence spreading models seldom take into account the time-related shifts. This paper proposes a minimum cost influence maximization algorithm based on the competitive transition probability. In the model, we set a one-dimensional vector for each node to record the probability that the node chooses each different competing influencer. In the process of propagation, the influence maximization on Competitive Linear Threshold (IMCLT) spreading model is proposed. This model does not determine by which competing influencer the node is activated, but sets different weights for all competing influencers. In the process of spreading, we select the seed nodes according to the cost function of each node, and evaluate the final influence based on the competitive transition probability. Experiments on different datasets show that the proposed minimum cost competitive influence maximization algorithm based on IMCLT spreading model has excellent performance compared with other methods, and the computational performance of the method is also reasonable.

• Journal of information and communication convergence engineering
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• v.16 no.4
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• pp.213-220
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• 2018

With the exponential developments of social network, their fundamental role as a medium to spread information, ideas, and influence has gained importance. It can be expressed by the relationships and interactions within a group of individuals. Therefore, some models and researches from various domains have been in response to the influence maximization problem for the effects of "word of mouth" of new products. For example, in reality, more than two related social groups such as commercial companies and service providers exist within the same market issue. Under such a scenario, they called social adversaries competitively try to occupy their market influence against each other. To address the influence maximization (IM) problem between them, we propose a novel IM problem for social adversarial players (IM-SA) which are exploiting the social network attributes to infer the unknown adversary's network configuration. We sophisticatedly define mathematical closed form to demonstrate that the proposed scheme can have a near-optimal solution for a player.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.40-45
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• 2015

Online social networks(OSN) are very popular nowadays. As OSNs grows, the commercial markets are expanding their social commerce by applying Influence Maximization. However, in reality, there exist more than two players(e.g., commercial companies or service providers) in this same market sector. To address the Influence Maximization problem between adversaries, we first introduced Influence Maximization against the social adversaries' problem. Then, we proposed an algorithm that could efficiently solve the problem efficiently by utilizing social network properties such as Betweenness Centrality, Clustering Coefficient, Local Bridge and Ties and Triadic Closure. Moreover, our algorithm performed orders of magnitudes better than the existing Greedy hill climbing algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2069-2085
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• 2021

A Social Networking Service (SNS) platform provides digital footprints to discover users' interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users' relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users' interest and social influence, where the user interest model relies on historical user behavior and the seed users' evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users' past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.471-482
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• 2014

Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1244-1246
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• 2016

Influence maximization problem is to select seed node set, which maximizes information spread in social networks. Greedy algorithm shows an optimum solution, but has a high computational cost. A few heuristic algorithms were proposed to reduce the complexity, but their performance in influence maximization is limited. In this paper, we propose general degree discount algorithm, and show that it has better performance while keeping complexity low.

• Journal of KIISE
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• v.44 no.10
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• pp.1105-1111
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• 2017

Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximizes the influence spread in social networks. However, one of the biggest problems of existing solutions for IM is that it takes too much time to select a k-seed set. This performance issue occurs at the micro and macro levels. In this paper, we propose a fast hybrid method that addresses two issues at micro and macro levels. Furthermore, we propose a path-based community detection method that helps to select a good seed set. The results of our experiment with four real-world datasets show that the proposed method resolves the two issues at the micro and macro levels and selects a good k-seed set.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2979-2995
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• 2017

In recent years, there has been an increasing number of studies focused on identifying a set of spreaders to maximize the influence of spreading in complex networks. Although the k-core decomposition can effectively identify the single most influential spreader, selecting a group of nodes that has the largest k-core value as the seeds cannot increase the performance of the influence maximization because the propagation sphere of this group of nodes is overlapped. To overcome this limitation, we propose a neighborhood coreness cover and discount heuristic algorithm named "NCCDH" to identify a set of influential and decentralized seeds. Using this method, a node in the high-order shell with the largest neighborhood coreness and an uncovered status will be selected as the seed in each turn. In addition, the neighbors within the same shell layer of this seed will be covered, and the neighborhood coreness of the neighbors outside the shell layer will be discounted in the subsequent round. The experimental results show that with increases in the spreading probability, the NCCDH outperforms other algorithms in terms of the affected scale and spreading speed under the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models. Furthermore, this approach has a superior running time.

• Journal of Technology Innovation
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• v.15 no.1
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• pp.175-201
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• 2007

Licensing contracts between partners in International Joint Ventures(IJV) have not only aspects of relation contract, which is interdependent and long-term cooperative relationships in interpartner but also aspects of discrete contract which is exposed to opportunistic risk caused by IJV partners who maximize individual profit instead of joint payoff maximization. In this circumstance, appropriate compensation structures such as lump-sum and royalty can reduce conflicts and spur interpartner cooperation. In addition, compensation structures that stipulate each party's rights, duties, and responsibilities under various sets of environmental conditions have strong implications for transaction cost minimization and joint payoff maximization. On the other hands, compensation structures such as lump-sum and royalty in IJV licensing contract have benefits and costs depending on IJV partners uncertainty, partner dependency, and environment uncertainty. Therefore, the purpose of this paper is to empirically show how partner uncertainty, partner dependence and environment uncertainty influence compensation structure chosen by licensor in IJV.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.416-417
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• 2018

본 논문에서는 기존 광고대행 유저 추천 서비스의 광고대행 유저 선출 방업이 갖는 문제를 해결하기 위해, 영향력 최대화 (Influence maximization) 연구 분야의 기술을 활용하여 (1) 유저들 간 단계적으로 파급되는 광고효과를 고려한 광고효과 최대화 방안을 제안한다. 나아가 보다 정확한 광고효과 평가를 위해, (2) 유저 간 지지도 (support) 및 (3) 유저의 컨텐츠 공유 (share) 점수를 정의하고 광고효과 최대화 방안에 반영하였다. 실 세계 데이터를 이용한 실험을 통해 제안하는 광고 대행 유저 선출 방안이 전통적인 선출 방안들보다 광고 효과가 더 큰 유저들을 선출함을 입증하였다.