Degree Centrality for Social Network with Opsahl Method

Yustiawan, Yoga; Maharani, Warih; Gozali, Alfian Akbar

doi:10.1016/j.procs.2015.07.559

Cited by 41 publications

(16 citation statements)

References 6 publications

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“…", identify the most important vertices within a graph or network. Popular applications of centrality help identify the most influential person in a social network, key infrastructure nodes in the Internet or urban networks, and super-spreaders of disease [92][93][94][95]. The indicator is a real-valued function on the vertices of a graph or network.…”

Section: Degree Centralitymentioning

confidence: 99%

Exploring Strategic Directions of Pandemic Crisis Management: A Text Analysis of World Economic Forum COVID-19 Reports

Nam

2021

Sustainability

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This study aims to understand the global environment of COVID-19 management and guide future policy directions after the pandemic crisis. To this end, we analyzed a series of the World Economic Forum’s COVID-19 response reports through text mining and network analysis. These reports, written by experts in diverse fields, discuss multidimensional changes in socioeconomic situations, various problems created by those changes, and strategies to respond to national crises. Based on 3897 refined words drawn from a morphological analysis of 26 reports (as of the end of 2020), this study analyzes the frequency of words, the relationships among words, the importance of specific documents, and the connection centrality through text mining. In addition, the network analysis helps develop strategies for a sustainable response to and the management of national crises through identifying clusters of words with similar structural equivalence.

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Section: Degree Centralitymentioning

confidence: 99%

Exploring Strategic Directions of Pandemic Crisis Management: A Text Analysis of World Economic Forum COVID-19 Reports

Nam

2021

Sustainability

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“…In fact, these behavioral features, which are known as a node strength and node degree, considered together showing the user behavior. Experimental results contended that if node strength (user behavior) has more impact factor than node degree, identified node will be have influence on others in social network sites (Yustiawan et al , 2015). Moreover, Liu and Zhu used level and degree model (LDM) to recognize influential nodes in the graph.…”

Section: Literature Reviewmentioning

confidence: 99%

New centrality measure for nodes based on user social status and behavior on Facebook

Forouzandeh

Sheikhahmadi

Aghdam

et al. 2018

IJWIS

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Purpose This paper aims to analyze the role of influential nodes on other users on Facebook social media sites by social and behavioral characteristics of users. Hence, a new centrality for user is defined, applying susceptible-infected recovered (SIR) model to identify influence of users. Results show that the combination of behavioral and social characteristics would be determined the most influential users that influence majority of nodes on social networks. Design/methodology/approach In this paper, the authors define a new centrality for users, considering node status and behaviors. Thus, this node has a high level of influence. Node social status includes node degree, clustering coefficient and average neighbors’ node, and social status of node refers to user activities on Facebook social media website such as sending posts and receiving likes from other users. According to social status and user activity, the new centrality is defined. Finally, through the SIR model, the authors explore infection power of nodes and their influences of other node in the network. Findings Results show that the proposed centrality is more effective than other centrality approaches, infecting more nodes in social network. Another significant point in this research is that users who have high social status and activities on Facebook are more influential than users who have only high social status on the Facebook social media. Originality/value The influence of user on others in social media includes two key factors. The first factor is user social status such as node degree and clustering coefficient in social media graph and the second factor is related to user social activities in social media sites. Most centralities focused on node social status without considering node behavior. This paper analyzes the role of influential nodes on other users on Facebook social media site by social and behavioral characteristics of users.

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“…Moreover, in order to prevent the privacy leakage, one user's (publisher's) privacy should only be shared with other ones (recipients) in a "correct" user set in OSNs [14][15][16]. The set, composed of numerous trusted recipients, can be updated based on the dynamic trust value that is a personal perception of a publisher to recipients.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Privacy Preserving Strategy for Word‐of‐Mouth Social Networks

Jing

Chen

Wen

2018

Wireless Communications and Mobile Computing

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An online social network (OSN) is a platform that makes people communicate with friends, share messages, accelerate business, and enhance teamwork. In the OSN, privacy issues are increasingly concerned, especially in private message leaks in word-of-mouth. A user’s privacy may be leaked out by acquaintances without user’s consent. In this paper, an integrated system is designed to prevent this illegal privacy leak. In particular, we only use the method of space vector model to determine whether the user’s private message is really leaked. Canary traps techniques are used to detect leakers. Then, we define a trust degree mechanism to evaluate trustworthiness of a communicator dynamically. Finally, we set up a new message publishing system to determine who can obtain the message of publisher. Secrecy performance analysis is provided to verify the effectiveness of the proposed message publishing system. Accordingly, a user in social networks can check whether other users are trustworthy before sending their private messages.

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Degree Centrality for Social Network with Opsahl Method

Cited by 41 publications

References 6 publications

Exploring Strategic Directions of Pandemic Crisis Management: A Text Analysis of World Economic Forum COVID-19 Reports

Exploring Strategic Directions of Pandemic Crisis Management: A Text Analysis of World Economic Forum COVID-19 Reports

New centrality measure for nodes based on user social status and behavior on Facebook

A Probabilistic Privacy Preserving Strategy for Word‐of‐Mouth Social Networks

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