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Autor     David Koelle, Jonathan Pfautz, Michael Farry, Zach Cox, Geoffrey Catto, Joseph Campolongo
Titel    Applications of Bayesian Belief Networks in Social Network Analysis
Sammlung    Proc. of the 4th Bayesian Modeling Applications Workshop during the 22nd Annual Conference on Uncertainty in Artificial Intelligence: UAI '06, July 13th, Cambridge, Massachusetts, 2006
Jahr    2006
Seiten    6
URL    http://www.cs.uu.nl/groups/DSS/UAI-workshop/Koelle.pdf

Literaturverz.   

no
Fußnoten    no
Fragmente    3


Fragmente der Quelle:
[1.] Nm/Fragment 028 08 - Diskussion
Zuletzt bearbeitet: 2012-04-21 14:50:27 Hindemith
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Social network analysis (SNA) primarily focuses on applying analytic techniques to the relationships between individuals and groups, and investigating how those relationships can be used to infer additional information about the individuals and groups (Degenne and Forse, 1999). There are a number of mathematical and algorithmic approaches that can be used in SNA to infer such information, including connectedness and centrality (Wasserman and Faust, 1994).

SNA is used in a variety of domains. For example, business consultants use SNA to identify the effective relationships between workers that enable work to get done; these relationships often differ from connections seen in an organizational chart (Ehrlich and Carboni, 2005). Law enforcement personnel have used social networks to analyze terrorist networks (Krebs, 2002; Stewart, 2001) and criminal networks (Sparrow, M. K., 1991). The capture of Saddam Hussein was facilitated by social network analysis: military officials constructed a network containing Hussein’s tribal and family links, allowing them to focus on individuals who had close ties to Hussein (Hougham, 2005).

Social network analysis (SNA) primarily focuses on applying analytic techniques to the relationships between individuals and groups, and investigating how those relationships can be used to infer additional information about the individuals and groups (Degenne & Forse, 1999). There are a number of mathematical and algorithmic approaches that can be used in SNA to infer such information, including connectedness and centrality (Wasserman & Faust, 1994).

SNA is used in a variety of domains. For example, business consultants use SNA to identify the effective relationships between workers that enable work to get done; these relationships often differ from connections seen in an organizational chart (Ehrlich & Carboni, 2005). Law enforcement personnel have used social networks to analyze terrorist networks (Krebs, 2006; Stewart, 2001) and criminal networks (Sparrow, 1991). The capture of Saddam Hussein was facilitated by social network analysis: military officials constructed a network containing Hussein’s tribal and family links, allowing them to focus on individuals who had close ties to Hussein (Hougham, 2005).

Anmerkungen

Identical, with the source not even being mentioned in Nm's thesis.

Sichter
(Graf Isolan), Hindemith

[2.] Nm/Fragment 033 01 - Diskussion
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1.5. LIMITATIONS

While traditional SNA has been used to successfully derive insights into a social network, it can be restrictive for a number of reasons. SNA assumes a well-formed social network, but real-world methods of data collection may not ensure that the resulting social network is complete and contains needed data. SNA focuses primarily on the existence of a relationship between nodes in the network, but not on attributes of that relationship or the nodes in the relationship. Furthermore, SNA does not explicitly consider the uncertainty of attributes on nodes or relationships. Finally, graph-theoretic algorithms used in SNA tend to focus on homogenous set of entities and relationships, making it difficult to analyze networks that involve a heterogeneous set of nodes connected by a variety of link types.

1.5.1 Issues in Data Collection [FN 7]

[FN 7] The text in this subsection is partially published in Memon Nasrullah and Larsen Henrik Legind. (2007e)

2. LIMITATIONS OF SOCIAL NETWORK ANALYSIS

While traditional SNA has been used to successfully derive insights into a social network, it can be restrictive for a number of reasons. SNA assumes a well-formed social network, but real-world methods of data collection may not ensure that the resulting social network is complete and contains needed data. SNA focuses primarily on the existence of a relationship between nodes in the network, but not on attributes of that relationship or the nodes in the relationship. Furthermore, SNA does not explicitly consider the uncertainty of attributes on nodes or relationships. Finally, graph-theoretic algorithms used in SNA tend to focus on a homogenous set of entities and relationships, making it difficult to analyze networks that involve a heterogeneous set of nodes connected by a variety of link types.

2.1 ISSUES IN DATA COLLECTION

Anmerkungen

Identical, with the source not being mentioned anywhere in Nm's thesis.

Sichter
(Graf Isolan), Hindemith

[3.] Nm/Fragment 038 04 - Diskussion
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1.5.2 Homogeneous Link Types

Social network analysis does not fully report the need to characterize different types of links. A link in a social network graph can represent a variety of concepts and relationships. For example: an evaluation (A likes B or A respects B), behavioural interaction, similarity, association, affiliation, physical connection, formal relations, (such as authority), and biological relations (Wasserman & Faust, 1994).

Links can be of different types. Link types can also indicate the strength of a particular concept. For example, link type “friendship” can be further classified four subtypes: “No contact”, “small talk and coffee”, “exchange of favours”, and “close ties” (Heran, F., 1987).

While traditional graph based algorithms used for SNA may incorporate analysis of different node and link types. However, they incline to be homogeneous within a network (i.e., considering a single node or link type per analysis), rather than being heterogeneous within a network (i.e., multiple link and node types). Further, graph based algorithms do not typically consider attributes on links or nodes.

2.2 HOMOGENEOUS NODE AND LINK TYPES

Social network analysis does not fully address the need to characterize different types of relationships. A social network graph can represent a variety of concepts through links, such as evaluation (A likes B, A respects B), behavioral interaction, transfers of material resources, association, affiliation, movement between places or statuses, physical connection, formal relations (such as authority), and biological relations (Wasserman & Faust, 1994).

Different links types can also indicate the strength of a particular concept. For example, one social network construction study offered subjects four choices to identify the intensity of their friendships: “No contact”, “small talk and coffee”, “exchange of favors”, and “close ties” (Heran, 1987).

While traditional graph-theoretic algorithms used for SNA may incorporate analysis of different node and link types, they tend to be homogeneous within a network (i.e., considering a single node or link type per analysis), rather than being heterogeneous within a network (i.e., multiple link and node types). Further, graph-theortic algorithms do not typically consider attributes on links or nodes [...]

Anmerkungen

Highly similar, with the source not being mentioned anywhere in Nm's thesis.

Sichter
(Graf Isolan), Hindemith

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