Earticle

초록

영어

In this paper, we propose that when a random network graph model that prefers to close two-hop chains and transform them to triangles as part of link formation. We hypothesize that such a model would generate random network graphs with high clustering coefficients and a larger variation in node degree, compared to that of the well-known Erdos-Renyi (ER) model. We refer to the proposed model as two-hop neighbor preference (THNP)-model that prefers to pair a node with any of its two-hop neighbors rather than to an arbitrary node. The probability of link formation is still governed by the formulation used to generate links in the ER model. We observe the THNP-model to generate random network graphs wherein the clustering coefficient of a node decreases with increase in node degree (resembling closely to several of the real-world network graphs), and the graphs still exhibit a Poisson-style distribution for the node degree and path length.

목차

Abstract
 1. Introduction
 2. Two-Hop Neighbor Preference (THNP) Model
  2.1. Relevance to Social Networks
  2.2. Example to Illustrate the Execution of the THNP Model
  2.3. Impact of the Preference for More Commonly Occurring Two-Hop Neighbors
 3. Simulations
  3.1. Erdos-Renyi Model
  3.2. Analytics Metrics
  3.3. Real-World Network Datasets
  3.4. Proximity Evaluation
 4. Related Work
 5. Conclusions
 References

키워드

저자

• Natarajan Meghanathan [ Jackson State University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605