Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

A Group Decision Making Approach Considering Self-Confidence Behaviors and Its Application in Environmental Pollution Emergency Management

Self-confidence as one of the human psychological behaviors has important influence on emergency management decision making, which has been ignored in existing methods. To fill this gap, we dedicate to design a group decision making approach considering self-confidence behaviors and apply it to the environmental pollution emergency management. In the proposed method, the self-confident fuzzy preference relations are utilized to express experts’ evaluations. This new type of preference relations allow experts to express multiple self-confidence levels when providing their evaluations, which can deal with the self-confidence of them well. To apply the proposed group decision making method to environmental pollution emergency management, a novel determination of the decision weights of experts is given combining the subjective and objective weights. The subjective weight can be directly assigned by organizer, while the objective weight is determined by the self-confidence degree of experts on their evaluations. Afterwards, by utilizing the weighted averaging operator, the individuals’ evaluations can be aggregated into a collective one. To do that, some operational laws for self-confident fuzzy preference relations are introduced. And then, a self-confidence score function is designed to get the best solution for environmental pollution emergency management. Finally, some analyses and discussions show that the proposed method is feasible and effective.The work was supported by National Key R&D Program of China (Grant No. 2017YFC0404600), National Natural Science Foundation of China (NSFC) under Grants (71871085, 71471056), Qing Lan Project of Jiangsu Province. Additionally, Xia Liu andWeike Zhang gratefully acknowledge the financial support of the China Scholarship Council (Nos. 201706710084, 201806240231)

Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making

The paper addresses the growing importance of Large Scale Group Decision Making (LSGDM) problems, focusing on hesitant fuzzy LSGDM. It introduces a Reliability Index-based Consensus Reaching Process (RI-CRP) to enhance efficiency. The proposed method assesses the ordinal consistency of decision makers' (DMs) information, measures deviation, and assigns a reliability index to DMs' opinions. An unreliable DMs management method is presented to filter out unreliable information. Additionally, an Alternative Ranking-based Clustering (ARC) method with hesitant fuzzy reciprocal preference relations is proposed to improve the efficiency of RI-CRP. The numerical example demonstrates the feasibility and effectiveness of the ARC method and RI-CRP for hesitant fuzzy LSGDM problems.Este artículo aborda la creciente importancia de los problemas de Toma de Decisiones en Grupo a Gran Escala (LSGDM), centrándose en el LSGDM difuso vacilante. Introduce un Proceso de Consenso Basado en Índices de Fiabilidad (RI-CRP) para mejorar la eficiencia. El método propuesto evalúa la consistencia ordinal de la información de los decisores, mide la desviación y asigna un índice de fiabilidad a las opiniones de los decisores. Se presenta un método de gestión de los decisores poco fiables para filtrar la información poco fiable. Además, se propone un método de agrupamiento alternativo basado en la clasificación (ARC) con relaciones de preferencia recíproca difusas vacilantes para mejorar la eficacia de RI-CRP. El ejemplo numérico demuestra la viabilidad y eficacia del método ARC y del RI-CRP para problemas LSGDM difusos vacilantes.Instituto Interuniversitario de Investigación en Data Science and Computational Intelligence (DaSCI

Concurrent bilateral negotiation for open e-markets: The Conan strategy

We develop a novel strategy that supports software agents to make decisions on how to negotiate for a resource in open and dynamic e-markets. Although existing negotiation strategies offer a number of sophisticated features, including modelling an opponent and negotiating with many opponents simultaneously, they abstract away from the dynamicity of the market and the model that the agent holds for itself in terms of ongoing negotiations, thus ignoring information that increases an agent’s utility. Our proposed strategy COncurrent Negotiating AgeNts (Conan) considers a weighted combination of modelling the market environment and the progress of concurrent negotiations in which the agent partakes. We conduct extensive experiments to evaluate the strategy’s performance in various settings where different opponents from the literature provide a competitive market. Our experiments provide statistically significant results showing how Conan outperforms the state-of-the-art in terms of the utility gained during negotiations

Towards a moderated-trust governance theory : explaining the dimensional structure of trust and distrust between a board of directors and a CEO

Trust is a central feature in corporate-governance theory and practice. While trust is advanced as a motivational factor in dominant organisational theories and practices, it is conspicuously absent in corporate-governance literature. At one extreme, scholars discount trust, which leads to theories that characterise the relationship between governance actors as goal-conflicted and as something that potentially exposes organisations to distress. At the other, scholars approach trust as a given and characterise governance actors as locked in mutually beneficial relationships that inspire trust, even if organisational distress also occurs. Neither extreme characterisation has been useful in explaining how governance actors organise themselves to avoid or escape financial distress. Using a multiple-casestudy method, underpinned by a critical-realist perspective, this study provides an explicit exploration of trust and its complement—distrust—and explains the complexity of the trust relationship between governance actors such as chief executive officers, board chairmen, and board directors. This study seeks to demonstrate that distrust, as characterised within agency theory, and trust, as portrayed within stewardship theory, detract from understanding effective board task-performance. Some scholars have relied on proxy variables such as board composition and financial performance to assess board task-performance, but this often leads to weak theoretical explanations. Moreover, this study specifies how optimal levels of trust and distrust could explain effective board task-performance, which scholars have shown partially contributes to financial performance. This study proposes that optimal trust and distrust between governance actors occurs where levels of trust and distrust are simultaneously high. This study develops a theory of moderated-trust governance that is underpinned by generative processes with supporting propositions. Therefore, this study contributes to literature on both trust and corporate governance.Thesis (PhD)--University of Pretoria, 2018.Gordon Institute of Business Science (GIBS)PhDUnrestricte