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Strategies of Self-Presentation I: Strategic Self-Presentation01:12

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Strategic self-presentation refers to individuals' intentional efforts to influence how others perceive them. This process is employed in various social and professional settings, such as job interviews, dating, politics, and legal contexts, where individuals seek to shape impressions to gain social or material advantages. While people generally present themselves in ways that align with their authentic characteristics, external factors, such as cognitive load, can hinder their ability to...
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Aligning actions are communicative strategies individuals employ to maintain social harmony and preserve personal identity in the face of potential disruptions to social norms. These actions are particularly important in managing social impressions when one's behavior might be seen as inappropriate, incompetent, or morally questionable.Types of Aligning ActionsThe three principal types of aligning actions are disclaimers, accounts, and apologies.DisclaimersDisclaimers are preventive; they are...
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A New Strategy in Boosting Information Spread.

Xiaorong Zhang1, Sanyang Liu2, Yudong Gong2

  • 1School of Mathematics and Statistics, Shaanxi Xueqian Normal University, Xi'an 710061, China.

Entropy (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new approach to influence maximization (IM) by incorporating boosting nodes alongside influential ones. The novel B-degree discount algorithm enhances cost-effectiveness and performance in realistic scenarios.

Keywords:
B-degree discount algorithmboosting information spreadrealistic propagation model

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Area of Science:

  • Information Science
  • Computer Science
  • Network Science

Background:

  • The influence maximization (IM) problem focuses on selecting a seed set to maximize information propagation within a budget.
  • Traditional IM models often overlook the role of 'boosting nodes' that can enhance influence spread efficiently.
  • Real-world propagation dynamics involve multiple node types, necessitating more comprehensive models.

Purpose of the Study:

  • To address the limitations of traditional IM by introducing the realistic budgeted influence maximization (RBIM) problem.
  • To incorporate both influential and boosting nodes for improved influence diffusion.
  • To develop an effective algorithm for solving the newly defined RBIM problem.

Main Methods:

  • Proposed a novel B-degree discount algorithm tailored for the RBIM problem.
  • The algorithm leverages cost-economic boosting nodes to amplify influence from initial seeds.
  • Evaluated the algorithm's performance against state-of-the-art methods on the RBIM problem.

Main Results:

  • The B-degree discount algorithm demonstrates superior performance in both effectiveness and efficiency for RBIM.
  • Incorporating boosting nodes significantly reduces the overall cost of influence maximization.
  • The proposed method offers a more realistic and cost-effective solution compared to existing approaches.

Conclusions:

  • The RBIM problem and the B-degree discount algorithm provide a more practical framework for influence maximization.
  • Utilizing boosting nodes is a key strategy for enhancing influence spread economically.
  • This research offers significant advancements in optimizing information diffusion in networks.