Opinion-Aware Influence Maximization: How to Maximize a Favorite Opinion in a Social Network?

Kermani, Mehrdad Agha Mohammad Ali; Ghesmati, Reza; Jalayer, Masoud

doi:10.1142/s0219525918500224

Cited by 5 publications

(8 citation statements)

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“…He proposed a linear programming relaxation-based method with a provable worst case bound [44]. Kermani et al proposed a non-linear bi-objective mathematical programming model to tackle an extension of influence maximization problem which is named opinion aware influence maximization [15]. They proposed a genetic algorithm to solve the problem and showed its efficiency comparing with some of the state-of-the-art algorithms.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…In addition, almost all the previous works (except [4,15]) on the diffusion problem have focused on locating the optimal (fixed number of ) nodes to maximize diffusion without considering the cost of seed nodes' activation In many contexts, however, efforts to leverage social influence to maximize diffusion in existing social networks are costly. Those who would diffuse their message may need to provide incentives to seed nodes, or invest heavily in education and influence of initial targets in order to start the process.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Objective function (15) is the developed version of objective function (2). The second term of ( 15), along with constraint (16), relates to minimizing the variability of scenarios which is identified by the variability measure presented by Leung et al [57].…”

Section: The Proposed Robust Optimization Modelmentioning

confidence: 99%

“…Based on [14,15], in this paper, the node's heterogeneity is directly measured by their "Social Skills". It means, we believe that the better social skill of a node the more probability of forwarding a received message.…”

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confidence: 99%

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A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

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Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Section: The Proposed Robust Optimization Modelmentioning

confidence: 99%

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confidence: 99%

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A robust optimization model for influence maximization in social networks with heterogeneous nodes

2021

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“…Information diffusion is a vast research domain and has expressed research interests of many fields, such as Physics, Biology, etc. [23], [35] [1]. For example, considering Facebook, where a user Sally updates her status or writes on a friend's wall about a new show in town that she enjoyed.…”

Section: Introductionmentioning

confidence: 99%

A hybrid algorithm based on community detection and multi attribute decision making for influence maximization

Jalayer

Azheian

Kermani

2018

Computers & Industrial Engineering

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Influence maximization problem is trying to identify a set of nodes by which the spread of influence, diseases or information is maximized. The optimization of influence by finding such a set is NP-hard problem and a key issue in analyzing complex networks. In this paper, a new greedy and hybrid approach based on a community detection algorithm and an MADM technique (TOPSIS) is proposed to cope with the problem, called, 'Greedy TOPSIS and Community-Based' (GTaCB) algorithm. The paper concisely introduces community detection and TOPSIS technique, then it presents the pseudo-code of the proposed algorithm. Afterwards, it compares the performance of the solution which is found by GTaCB with some well-known greedy algorithms, based on Degree Centrality, Closeness Centrality, Betweenness Centrality, PageRank as well as TOPSIS, from two aspects: diffusion quality and diffusion speed. In order to evaluate the performance of GTaCB, computational experiments on nine different types of real-world networks are provided. The tests are conducted via one of the renowned epidemic diffusion models, namely, Susceptible-Infected-Recovered (SIR) model. The simulations exhibit that in most of the cases the proposed algorithm significantly outperforms the others, chiefly as number of initial nodes or probability of infection increases.

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Analyzing the effect of location, communication regime, and demographic faultline on team cohesion

Aliahmadi

Makui

Naeini

2019

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Purpose Building on the Lau and Murnighan’s theory of fault line strength, Flache and Mäs (2008b) proposed a computational opinion dynamics model to explore the effect of demographic fault line strength on team cohesion. This study aims to extend the Flache–Mäs (FM) model to incorporate geographical location and the dyadic communication regime in opinion formation process. More specifically, we make spatially proximate agents more likely to interact with each other in the dyadic communication regime. Our results show that when agents update their opinion after each pairwise encounter, opinion polarization is lower at steady state compared to when they update their opinion after interacting with all agents. In addition, if nearby agents are more likely to interact with each other, we see greater polarization compared to the FM model with the dyadic communication regime. An immediate policy implication of this result is that organizational managers should design work space in a way that encourage wider communications between members of a team and avoid geographically local communication. Design/methodology/approach We introduce our computational models to study the effect of location and the dyadic communication regime on team performance (as measured by agents’ opinions on various work-related issues) in the presence of a strong demographic fault line. Our models are extensions of the FM model. For clarification purposes, first we describe the FM model and then elaborate our extensions. Findings The most important finding of this paper is that the timing of interactions plays an important role in steady state of opinion space in a given population. The reason can be traced to the path-dependent nature of social systems, in which initial adopters of a certain opinion or an ideology can significantly change the final configuration of a population. For example, if an early adopter of a given work-related issue in an organization has an extremely positive view toward that issue, and s/he interacts with nearby employees who have similar demographic attributes, we would expect to find an extreme opinion cluster with respect to that issue after a while. However, depending on factors that affect the timing of interaction between individuals, we would expect different outcome in the same organization. If, for instance, more extreme people are more likely to interact, the results would be different compared to when moderate agents are more likely to interact. Originality/value One immediate policy implication of the results of this paper is that organizational managers should design work space in a way that encourage wider communications between members of a team and avoid geographically local communication, if they are to temper the negative effect of a strong demographic fault line. However, they should be cautious and take other related findings into account to avoid undesirable outcomes. For example, according to Flache and Mäss’s results, managers can also initially encourage discussion within demographically homogenous groups and avoid controversial work-related issues. In addition, previous studies showed that more contacts between agents may increase opinion polarization. Our results provide no evidence for more complex and modern organizational designs where individuals or teams do not have a fixed location or stable geographical pattern. For instance, in a modern car manufacturing shop floor, it is possible that workers have to move with cars, or operational engineers have to move between different sections and places. Furthermore, there may be a flexible and dynamic work schedule for workers such that they share a same work station but in different time, which requires a more complex model than what we presented in this paper. In this sense, the geographical setting analyzed in this paper should not be generalized to all organizations or companies. We also have no evidence about other critical factors that might affect the communication and activation regime of individuals. For example, one could imagine a case that workers with the same level of skill in a specific work-related issue are more likely to interact with each other. Moreover, some specific organizational structures could impose additional restrictions on who can/should interact with whom.

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Opinion-Aware Influence Maximization: How to Maximize a Favorite Opinion in a Social Network?

Cited by 5 publications

References 59 publications

A robust optimization model for influence maximization in social networks with heterogeneous nodes

A robust optimization model for influence maximization in social networks with heterogeneous nodes

A hybrid algorithm based on community detection and multi attribute decision making for influence maximization

Analyzing the effect of location, communication regime, and demographic faultline on team cohesion

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