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Volumn 85, Issue 4, 2012, Pages

Enhancing topology adaptation in information-sharing social networks

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE MODELS; BEST FIT; EMPIRICAL ANALYSIS; INFORMATION DIFFUSION; INFORMATION OVERLOADS; INFORMATION SOURCES; LOCAL SEARCH; NETWORK CONFIGURATION; SOCIAL COMMUNITIES; SOCIAL NETWORKING SITES; SOCIAL NETWORKS; SOCIAL SYSTEMS; SOURCE SELECTION; TOPOLOGICAL FEATURES; TOPOLOGY ADAPTATION;

EID: 84860499020     PISSN: 15393755     EISSN: 15502376     Source Type: Journal    
DOI: 10.1103/PhysRevE.85.046108     Document Type: Article
Times cited : (19)

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    • There are more realistic models, in which, for example, users have different scopes of interest or tastes are unevenly distributed in the population. However, these assumptions may lead to bias in the leader selection process, e.g., the systematic preference for some special users, which might complicate our understanding of the local aspect of the topology adaptation.
    • There are more realistic models, in which, for example, users have different scopes of interest or tastes are unevenly distributed in the population. However, these assumptions may lead to bias in the leader selection process, e.g., the systematic preference for some special users, which might complicate our understanding of the local aspect of the topology adaptation.
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    • A) possible optimal leaders who differ exactly in two taste vector elements, the optimal state is unique only if L=N. If LN, the average differences are greater than two, even in the optimal state.
    • A) possible optimal leaders who differ exactly in two taste vector elements, the optimal state is unique only if L = N. If L < N, there are different possible optimal states, which are equivalent in term of quality of leaders' assignment but may differ under other properties like r and c. Initial conditions and users' dynamics determine the particular equilibrium state. If L > N, the average differences are greater than two, even in the optimal state.
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    • Other measures of system's performance (like precision and accuracy) exist and can be derived from 1-α and 1-β in our artificial setting. We chose to focus on specificity and sensitivity as they show the strongest dependence on how the neighborhood is defined and hence on the resulting local network topology.
    • Other measures of system's performance (like precision and accuracy) exist and can be derived from 1 - α and 1 - β in our artificial setting. We chose to focus on specificity and sensitivity as they show the strongest dependence on how the neighborhood is defined and hence on the resulting local network topology.
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    • For the similarity estimation, we adopt the same definition as in the adaptive model, Eq. .
    • For the similarity estimation, we adopt the same definition as in the adaptive model, Eq..
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    • iα=-1.
    • i α = - 1.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.