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132,921
Latent space approaches to social network analysis.
- Journal of the American Statistical Association,
, 2002
"... Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a speci ed relation be ..."
Abstract
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Cited by 318 (20 self)
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Network models are widely used to represent relational information among interacting units. In studies of social networks, recent emphasis has been placed on random graph models where the nodes usually represent individual social actors and the edges represent the presence of a speci ed relation
Semantic Social Network Analysis
"... Abstract: Social Network Analysis (SNA) tries to understand and exploit the key features of social networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge social network. Classical methods from social network analysis (SNA) have b ..."
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Abstract: Social Network Analysis (SNA) tries to understand and exploit the key features of social networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge social network. Classical methods from social network analysis (SNA) have
Social network analysis with sna
- Journal of Statistical Software
, 2008
"... Abstract Modern social network analysis-the analysis of relational data arising from social systems-is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical compu ..."
Abstract
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Cited by 33 (0 self)
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Abstract Modern social network analysis-the analysis of relational data arising from social systems-is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical
Different Aspects of Social Network Analysis
- In Proceedings of the 2006 IEEE/WIC/ACM International Conference on the Web Intelligence (WI-06), Hong Kong
, 2006
"... Abstract — A social network is a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange. Social network analysis focuses on the analysis of patterns of relationships among people, organizations, sta ..."
Abstract
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Cited by 31 (0 self)
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Abstract — A social network is a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange. Social network analysis focuses on the analysis of patterns of relationships among people, organizations
UNDERSTANDING SOCIAL NETWORK ANALYSIS
, 2012
"... Social Network Analysis (SNA) of agricultural research institutions in Malawi was performed to investigate networking and knowledge sharing in relation to the implementation of the Agricultural Sector Wide Approach (ASWAp). SNA helps explain how the different actors relate to one another, and how th ..."
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Social Network Analysis (SNA) of agricultural research institutions in Malawi was performed to investigate networking and knowledge sharing in relation to the implementation of the Agricultural Sector Wide Approach (ASWAp). SNA helps explain how the different actors relate to one another, and how
Market Analysis of „Social Network Analysis “ Books
"... Social network analysis, market overview, workbooks, didactic and content analysis ..."
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Social network analysis, market overview, workbooks, didactic and content analysis
T.: Evaluating CSCL log files by social network analysis
- In: CSCL ’99: Proceedings of the conference on Computer support for collaborative learning. International Society of the Learning Sciences
, 1999
"... Evaluating CSCL log files by social network analysis ..."
Abstract
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Cited by 48 (1 self)
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Evaluating CSCL log files by social network analysis
Reputation and Social Network Analysis In Multi-Agent Systems
, 2002
"... The use of previous direct interactions is probably the best way to calculate a reputation but, unfortunately this information is not always available. This is especially true in large multi-agent systems where interaction is scarce. In this paper we present a reputation system that takes advantage, ..."
Abstract
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Cited by 199 (12 self)
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, among other things, of social relations between agents to overcome this problem.
Social Network Analysis for Routing in Disconnected Delay-tolerant MANETs
, 2007
"... Message delivery in sparse Mobile Ad hoc Networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low end-to-end delay in a disconnected network graph where nodes may move fr ..."
Abstract
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Cited by 276 (1 self)
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where nodes are not required to exchange information about the entire network topology, but only locally available information is considered. Then SimBet Routing is proposed which exploits the exchange of pre-estimated ‘betweenness’ centrality metrics and locally determined social ‘similarity
Applications of Bayesian Belief Networks in Social Network Analysis
- In Proceedings of 4th Bayesian Modeling Applications Workshop at the 22nd Annual Conference on Uncertainty in AI: UAI '06
, 2006
"... In this paper, we discuss the use of Bayesian belief networks as a tool for enhancing social network analysis. Traditional social network analysis (SNA) primarily uses graph-theoretic algorithms to compute properties of nodes in a network. However, these algorithms assume a degree of completeness an ..."
Abstract
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Cited by 5 (5 self)
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In this paper, we discuss the use of Bayesian belief networks as a tool for enhancing social network analysis. Traditional social network analysis (SNA) primarily uses graph-theoretic algorithms to compute properties of nodes in a network. However, these algorithms assume a degree of completeness
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