Influence maximization in online social network using different centrality measures as seed node of information propagation

Dey, Paramita; Chaterjee, Agneet; Roy, Sarbani

doi:10.1007/s12046-019-1189-7

Cited by 26 publications

(6 citation statements)

References 23 publications

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“…The measures of centrality are understood through a set of concepts that are related to the contribution of the node to the structure of the network [35]. Therefore, centrality can be considered a structural node [38] and the interpretation is conditioned on the importance and type of data analyzed. In the analysis of social networks, the central nodes are interpreted as the most influential or leaders [39], or even have greater autonomy, visibility, or involvement [35].…”

Section: Discussionmentioning

confidence: 99%

Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market

da Cruz Correia,

Mendes dos Reis,

Amorim

et al. 2024

Logistics

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Background: The coffee industry is one of the most important world supply chains, with an estimated consumption of two billion cups daily, making it the most consumed beverage worldwide. Coffee beans are primarily grown in tropical countries, with Brazil accounting for almost 50% of the production. The objective of this study is to examine the Brazilian trade between 2018 and 2022, focusing on state producers, logistical corridors, and importer countries. Methods: The methodology approach revolves around a quantitative method using Social Network Analysis measures. Results: The results reveal a massive concentration in local production (99.5%—Minas Gerais), port movements (99.9%—Santos, Itaguai, and Rio de Janeiro), and country buyers (80.9%—the United States, United Kingdon, and Japan). Conclusions: The study concludes that the Brazilian green coffee supply chain relies on a fragile and overloaded logistical network. Due to that, this study indicates that the stakeholders and decision-makers involved must consider this high concentration of production in some areas and companies. They must also address the bottlenecks in logistical corridors and the fierce competition involved in acquiring and processing Brazilian coffee production because these factors can drastically affect the revenue of the companies operating in this sector.

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Section: Discussionmentioning

confidence: 99%

Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market

da Cruz Correia,

Mendes dos Reis,

Amorim

et al. 2024

Logistics

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“…The success of pretraining learning methods in the supervised learning domain has spurred interest in the reinforcement learning (RL) domain to study whether the same paradigms can be adapted to RL algorithms. General pretraining RL can include broad directions, such as Reward-Free RL [356,357,358,359], Goalcondition RL [360,361,362], and Representation Learning in RL [363,364,365,366]. Here we focus the Representation Learning in RL.…”

Section: E Pfms For Reinforcement Learningmentioning

confidence: 99%

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

Zhou¹,

Li²,

Li³

et al. 2023

Preprint

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The Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A pretrained foundation model, such as BERT, GPT-3, MAE, DALLE-E, and ChatGPT, is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications. The idea of pretraining behind PFMs plays an important role in the application of large models. As a type of transfer learning paradigm, pretraining is applied in computer vision with frozen and fine-tuning techniques, showing promising performance. Word embedding in the natural language process can be also regarded as a type of pertaining, but it suffers many problems such as polysemy. Different from previous methods that apply convolution and recurrent modules for feature extractions, the generative pre-training (GPT) method applies Transformer as the feature extractor and is trained on large datasets with an autoregressive paradigm. Similarly, the BERT apples transformers to train on large datasets as a contextual language model. Recently, the ChatGPT shows promising success on large language models, which applies an autoregressive language model with zero shot or few show prompting. With the extraordinary success of PFMs, AI has made waves in a variety of fields over the past few years. Considerable methods, datasets, and evaluation metrics have been proposed in the literature, the need is raising for an updated survey.This study provides a comprehensive review of recent research advancements, current and future challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities. We first review the basic components and existing pretraining in natural language processing, computer vision, and graph learning. We then discuss other advanced PFMs for other data modalities and unified PFMs considering the data quality and quantity. Besides, we discuss relevant research about the fundamentals of the PFM, including model efficiency and compression, security, and privacy. Finally, we lay out key implications, future research directions, challenges, and open problems. We hope this survey can shed light on the research of PFMs on the scalability, reasoning ability, cross-domain ability, user-friendly interactive ability, security and privacy-preserving ability for artificial general intelligence.

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“…The impact of contagion is influenced by the network's topology, which provides certain nodes with advantages in terms of spreading the contagion. However, it is important to note that there is currently no universal model, formal procedure, or methodology for identifying the optimal measurements in a network (Dey et al 2019).…”

Section: Network Centrality Measuresmentioning

confidence: 99%

Evaluating the Hierarchical Contagion of Economic Policy Uncertainty among the Leading Developed and Developing Economies

2023

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An array of global events, including the global financial crisis, natural disasters, and the recent coronavirus pandemic, have consistently shown the vulnerability of global systems and humans to externally undesirable contagions. In order to further provide alternative approaches to information valuation, this study utilized the economic policy uncertainty (EPU) of 21 leading developed and developing economies (Australia, Brazil, Canada, Chile, China, Colombia, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Korea, Netherlands, Russia, Spain, Sweden, the United Kingdom, and the United States of America) over the period January 1997 to May 2021. The information theory reveals the hierarchy of degrees of randomness in the EPU indices; it shows the information flow among the EPU indices through the mutual information metric and the graphical illustration of the information flows using network theory. Importantly, the Entropy measures indicate higher predictability of the Netherlands and Ireland’s EPU indices, suggesting that they have less randomness than other indices. Contrarily, Greece and the United Kingdom share the lowest predictability of the EPU indices. Moreover, the complex networks analysis shows that the EPU indices is generally shaped by geographic location. In order of significance, the United States of America’s EPU index exhibits the strongest correlation with other countries’ EPU indices and followed by the EPU indices of France, the United Kingdom (UK), and Germany. In general, the result of the investigation communicates relevant policy measures that potentially ameliorate shocks from external contagions.

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Influence maximization in online social network using different centrality measures as seed node of information propagation

Cited by 26 publications

References 23 publications

Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market

Impacts of Brazilian Green Coffee Production and Its Logistical Corridors on the International Coffee Market

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

Evaluating the Hierarchical Contagion of Economic Policy Uncertainty among the Leading Developed and Developing Economies

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