Analysis of Statistical and Structural Properties of Complex networks with Random Networks

Vairachilai, S.; Devi, M. K. Kavitha; Raja, M.

doi:10.18576/amis/110116

Cited by 2 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the area of social networks, the normal separation starting with one node then onto the next is little contrasted with the networks measure, known as the Small World Effect. The size of a network is generally estimated by identifying the number of vertexes(nodes) in the social network [12]. This Small World Effect is similar to the logic behind Collaborative Filtering, where items are recommended to the current user based on their common likes.…”

Section: Fig 1: Small World Network Structure Source: Adapted From [8]mentioning

confidence: 99%

See 1 more Smart Citation

Complex Network based Recommender System

Kingsley¹,

Francis²,

Appiah³

et al. 2019

IJCA

View full text Add to dashboard Cite

The structural abstraction of Recommender Systems has a high resemblance as that of a Complex Network(CN). The underlining principles of the different types of Recommender Systems, such as Collaborative Filtering, Content-Based, Knowledge-Based, Utility-Based, and Hybrid are similar to the attributes and theories of Complex Networks. Considering the enormity of computation in Recommender Systems logic formulation and it's a structural similarity with Complex Networks, the research work seeks to highlight attributes of Complex Networks and Recommender Systems to suggest the applicability of theories, and principles in CN's that can be considered in the construction of Recommender Systems. The research further elaborates on matrices for checking the correctness of the use of these theories.

show abstract

Section: Fig 1: Small World Network Structure Source: Adapted From [8]mentioning

confidence: 99%

“…The Degree centrality measure checks what number of neighbors a vertex has [12]. If the system is directed, two variants of the measure are considered.…”

Section: Degree Centralitymentioning

confidence: 99%