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Autor     Adam Perer, Ben Shneiderman
Titel    Balancing Systematic and FlexibleExploration of Social Networks
Zeitschrift    IEEE Transactions on visualization and computer graphics
Verlag    IEEE Computer Society
Datum    September/October 2006
Jahrgang    12
Nummer    5
Seiten    693-700
Anmerkung    The freely downloadable version of the paper contains no page numbers. Those can be inferred easily, however, as paper contains 8 pages.
ISSN    1077-2626
URL    http://hcil.cs.umd.edu/trs/2006-25/2006-25.pdf

Literaturverz.   

no
Fußnoten    no
Fragmente    6


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Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a combination of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help intelligence analysts understand social terrorist networks more effectively. Abstract— Social network analysis (SNA) has emerged as a powerful method for understanding the importance of relationships in networks. However, interactive exploration of networks is currently challenging because: (1) it is difficult to find patterns and comprehend the structure of networks with many nodes and links, and (2) current systems are often a medley of statistical methods and overwhelming visual output which leaves many analysts uncertain about how to explore in an orderly manner. This results in exploration that is largely opportunistic. Our contributions are techniques to help structural analysts understand social networks more effectively
Anmerkungen

Almost 1-to-1 copy of the first part of the abstract of Perer & Shneiderman (2006). One of the few changes has been to introduce the word "terrorist".

The source is not given.

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(Hindemith) Agrippina1

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6.2 EXPLORING TERRORIST NETWORKS

Understanding networks is a basically a difficult process. It is difficult to visualize, navigate, and most problematic, to detect patterns in terrorist networks. Despite all of these challenges, the network perspective is appealing. It is to mention that network analysts also focus on relationships instead of just the individual nodes (like we did in this study); putting nodes together is just as important as the nodes themselves. Before to this perspective, social research focused largely on attributes and neglected the social part of behaviour (how individuals interact and the influence they have on each other) (Freeman L. C., 2004). Using techniques from the social network community, analysts can easily find patterns in the structure, witness the flow of resources through a network, and learn how individuals are influenced by their surroundings.

Understanding networks is an inherently difficult process. It is difficult to visualize, navigate, and most problematic, find patterns in networks. Despite all of these challenges, the network perspective is appealing. Network analysts focus on relationships instead of just the individual elements; how the elements are put together is just as important as the elements themselves. Prior to this perspective, social research focused largely on attributes and neglected the social part of behavior (how individuals interact and the influence they have on each other) [EN 12]. Using techniques from the social network community, analysts can find patterns in the structure, witness the flow of resources through a network, and learn how individuals are influenced by their surroundings.

[EN 12] L. C. Freeman, The Development of Social Network Analysis: A Study in the Sociology of Science, Empirical Press, 2004.

Anmerkungen

Only minor changes. The source is not given anywhere in the thesis. The source pages are not numbered, this is in the first column of the first page.

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(Hindemith), WiseWoman

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In practice, a network visualization of a domain can be a messy one, particularly when the network is very large. Visualizations are useful to influence the powerful perceptual skills of humans, but overlapping links and labels of nodes mostly weaken this approach. It is to note that say that I cannot say researchers studying networks are completely lost. The techniques from sociology to graph theory in the literature can be found that allow analysts to detect interesting structures in networks. Analysts might pursue a tight-coupled community of the actors, or the brokers between them, or the most powerful actors – and there are a number of complex algorithms for detecting these behaviours.

It is worthwhile to mention that more mature fields in the area known as biology have developed techniques to train beginners and guarantee uniformity among analysts. The techniques are complete, so if two analysts are able to present with the same data, they most probably reach at the same conclusion. Though, in the social networks area, different networks are required to be analyzed in a different way. The wide spread of an epidemic among small towns is not necessarily the same as a spread of a business disaster on world markets (Watts D. J., 2003). From the above discussion, it is found that since there is no systematic way to understand networks; researchers need to be able to discover features to order to see patterns.

Freeman proposes that social network analysts pursue to expose two types of patterns in networks: i) those that disclose subsets of nodes that are ordered into unified social communities, and ii) those that disclose subsets of nodes that occupy comparable social roles (Freeman L. C., 2004b). A lot of work has been carried over the 6-7 decades to discover such patterns. Social Network Analysis: Methods and Applications, by Wasserman and Faust, is the main contribution used reference book for social scientists/ students (Wasserman S. and K. Faust, (1994). The book provides details and a review of SNA methods and description of the field.

In practice, a network visualization of a domain can be a messy one, particularly when the network is large. Visualizations are useful to leverage the powerful perceptual abilities of humans, but overlapping links and illegible labels of nodes often undermine this approach. This is not to suggest that researchers studying networks are completely lost. There is a rich history of techniques from sociology to graph theory that allow analysts to find interesting features in networks. Analysts might seek a tight-knit community of individuals, or the gatekeepers between them, or the most centrally powerful entities – and there are a variety of sophisticated algorithms for finding these traits.

More mature fields, such as field biology, have developed systematic methods to train novices and ensure consistency among analysts. The methods are complete and repeatable, so if two analysts are presented with the same data, they should reach the same conclusion. However, in the social networks field, different networks need to be analyzed differently. The spread of an epidemic among villages is not necessarily the same as a spread of a financial crisis on world markets [34]. Since there is no systematic way to interpret networks, users need to be able to flexibly explore features to discover patterns.

[...]

Freeman suggests that social network analysts seek to uncover two types of patterns in networks: (1) those that reveal subsets of nodes that are organized into cohesive social groups, and (2) those that reveal subsets of nodes that occupy equivalent social positions, or roles [FN 11]. There is a large body of work over the past 60 years to uncover such patterns. Social Network Analysis: Methods and Applications, by Wasserman and Faust, is perhaps the most widely used reference book for structural analysts [FN 33]. The book presents a review of network analysis methods and an overview of the field.


[FN 11] L. C. Freeman, "Graphic Techniques for Exploring Social Network Data", Models and Methods in Social Network Analysis, in P. J. Carrington, J. Scott and S. Wasserman, eds., Cambridge University Press, Cambridge, 2004

[FN 33] S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications, Cambridge University Press, 1994.

[FN 34] D. J. Watts, Six Degrees: The Science of a Connected Age, W.W. Norton & Company, New York, 2003.

Anmerkungen

Slight modifications. Also literature references have been copied. The source is not given anywhere in the thesis.

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(Hindemith), WiseWoman

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It is to be noted that the visualizations of social networks have been used to support SNA from the beginning Freeman L. C., (2000). Visualization of networks is more important because it is a natural way to communicate connectivity and allows for fast pattern recognition by human eyes. On the contrary, there are a number of challenges when visualizing networks (Battista et al., 1999; Herman et al., 2000). A number of layout algorithms discuss how to calculate the position of each and every node and the curve of each link in order to minimize link crossings and observe to visual principles. The algorithms are not large in number, and it is very difficult if the notes are very large then visualization is very difficult (Ham F. van, 2005).

Numerous techniques try to use available display more proficiently by distorting the graph/ network. One of the popular techniques is fisheye technique, which allows users to observe a focus area in detail, For example, read for further details (Munzner T., (1997). Another technique is Multiscale graph abstraction that reserves global structure, but navigation became difficult because clusters are obviously contracted and expanded, more information can be found in (Auber, et al, 2003; Parker G., G. Franck and C. Ware, 1998). Recent work associates these two techniques with fisheyeviews to decrease the number of displayed nodes while protecting the network structure (Gansner, E. R., Y. Koren and S. North, 2005). In addition, Ham Van and Van Wijk (2004) also combine distortion strategies for highly connected small-world networks.

A number of software tools designed to assist analysts to understand social networks, such as (Borgatti, S., M. G. Everett and L. C. Freeman, 2006; Brandes, U. and D. Wagner, 2003; de Nooy W., A. Mrvar and V. Batageli, 2005). The tools offer exciting techniques that users can use on networks. Though, the techniques are mostly a combination of statistical methods and visual output that put many analysts unclear about in what way and how to discover in a systematic manner.

Visualizations of social networks have been used to aid SNA from the beginning [EN 13]. The visualization of networks is important because it is a natural way to communicate connectivity and allows for fast pattern recognition by humans. However, there are great challenges when visualizing networks [EN 9, EN 18]. There are many layout algorithms that attempt to calculate the position of each node and the curve of each link to minimize link crossings and adhere to aesthetic principles. These algorithms fall short, however, when the number of nodes is larger than several hundred and the large number of overlapping links makes it hard to judge connectivity [EN 31].

Several approaches attempt to more efficiently use available display space by distorting the graph. Fisheye techniques allow users to examine a focus area in great detail, but also tend to obscure the global structure of networks, e.g. [EN 21, EN 23]. Multiscale graph abstraction is another technique that preserves global structure, however navigation is difficult because clusters are explicitly contracted and expanded, e.g. [EN 2, EN 26]. Recent work combines these two approaches with topological fisheye views to reduce the number of displayed nodes while preserving the network structure [EN 14]. Van Ham and van Wijk also combine distortion strategies for highly connected, small-world networks [EN 32].

There are a number of software tools designed to help analysts understand social networks, such as [5, 7, 8]. These tools often feature an impressive number of analysis techniques that users can perform on networks. However, they are also often a medley of statistical methods and overwhelming visual output that leaves many analysts uncertain about how to explore in an orderly manner.


[EN 2] D. Auber, Y. Chiricota, F. Jourdan and G. Melancon, "Multiscale Visualization of Small World Networks", IEEE Symposium on Information Visualization, pp. 75-81, 2003.

[EN 5] S. Borgatti, M. G. Everett and L. C. Freeman, UCINET 6, Analytic Technologies, 2006.

[EN 7] U. Brandes and D. Wagner, "visone - Analysis and Visualization of Social Networks", Graph Drawing Software,in M. Junger and P. Mutzel, eds., Springer-Verlag, 2003.

[EN 8] W. de Nooy, A. Mrvar and V. Batageli, Exploratory Social Network Analysis with Pajek, Cambridge University Press, Cambridge, 2005.

[EN 9] G. Di Battista, P. Eades, R. Tamassia and I. G. Tollis, Graph Drawing: Algorithms for the Visualization of Graphs, Prentice Hall, New Jersey, 1999.

[EN 13] L. C. Freeman, "Visualizing Social Networks", Journal of Social Structure, 2000.

[EN 14] E. R. Gansner, Y. Koren and S. North, "Topological Fisheye Views for Visualizing Large Graphs", IEEE Transactions on Visualization and Computer Graphics, 11, pp. 457-468, 2005.

[EN 18] I. Herman, G. Melancon and M. S. Marshall, "Graph visualization and navigation in information visualization: A survey", IEEE Transactions on Visualization and Computer Graphics, 6, pp. 23-43, 2000.

[EN 19] H. Kang, C. Plaisant, B. Lee and B. B. Bederson, "NetLens: Iterative Exploration of Content-Actor Network Data", IEEE Symposium on Visual Analytics Science and Technology, 2006.

[EN 21] J. Lamping and R. Rao, "The hyperbolic browser: A Focus+Context Technique for Visualizing Large Hierarchies", Journal of Visual Languages and Computing, 6, 1995.

[EN 23] T. Munzner, "H3: Laying Out Large Directed Graphs in 3D Hyperbolic Space", IEEE Symposium on Information Visualization, pp. 2-10, 1997.

[EN 26] G. Parker, G. Franck and C. Ware, "Visualization of Large Nested Graphs in 3D: Navigation and Interaction", Journal of Visual Languages and Computing, pp. 299-317, 1998.

[EN 31] F. van Ham, Interactive Visualization of Large Graphs, Technische Universiteit Eindhoven, 2005.

[EN 32] F. van Ham and J. J. van Wijk, "Interactive Visualization of Small World Graphs", IEEE Symposium on Information Visualization, 2004.

Anmerkungen

Slight modifications. Also all literature references stem from the source, which is not given anywhere in the thesis. et al is sometimes in italics, sometimes not.

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(Hindemith), WiseWoman

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[SNA is known as basically a] deductive assignment, and a user’s investigative procedure can be confused by having to navigate between separate analysis and visualization packages.

Most recently a number of projects focus to improve interactive exploration with networks. For example, GUESS is well known as a novel graph exploration system that combines an interpreted language with a graphical front end Adar E., (2006). TreePlus allows users to explore graphs using more comprehensible enhanced tree layouts (Lee, B. et al. 2006). NetLens allows users to discover an actor-event network using iterative queries and histograms (Kang, H. et al (2006). In addition, Ghoneim et al. (2004) presented matrix-based visualizations. JUNG is a JAVA toolkit which delivers analysts with a framework to construct their own SNA tools (O'Madadhain, J., 2005).

SNA is an inherently deductive task, and a user’s exploratory process can be distracted by having to navigate between separate analysis and visualization packages.

Recently, there have been several projects focusing on improving interactive exploration with networks. Among them, GUESS is a novel graph exploration system that combines an interpreted language with a graphical front end [EN 1]. TreePlus allows users to explore graphs using more comprehensible enhanced tree layouts [EN 22]. NetLens allows users to explore an actor-event network using iterative queries and histograms [EN 19]. Ghoneim et al. presented the promise of using matrix-based visualizations instead of node-link diagrams [EN 15]. JUNG is a JAVA toolkit that provides users with a framework to build their own social network analysis tools [EN 25].


[EN 1] E. Adar, "GUESS: A Language and Interface for Graph Exploration", ACM Conference on Human Factors in Computing Systems, 2006.

[EN 15] M. Ghoniem, J.-D. Fekete and P. Castagliola, "A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations", IEEE Symposium on Information Visualization, 2004.

[EN 19] H. Kang, C. Plaisant, B. Lee and B. B. Bederson, "NetLens: Iterative Exploration of Content-Actor Network Data", IEEE Symposium on Visual Analytics Science and Technology, 2006.

[EN 22] B. Lee, C. S. Parr, C. Plaisant, B. B. Bederson, V. D. Veksler, W. D. Gray and C. Kotfila, "TreePlus: Interactive Exploration of Networks with Enhanced Tree Layouts", IEEE Transactions on Visualization and Computer Graphics, 2006.

[EN 25] J. O'Madadhain, D. Fisher, P. Smyth, S. White and Y.-B. Boey, "Analysis and Visualization of Network Data using JUNG", Journal of Statistical Software, VV, 2005.

Anmerkungen

Minor adjustments, also literature references have been copied. The source is not given anywhere in the thesis.

Sichter
(Hindemith), WiseWoman

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This chapter presents Investigative Data Mining Toolkit: iMiner, which is believed more than just another terrorist social network analysis tool because it balances systematic and flexible exploration. To help users systematically examine measures, iMiner applies new mathematical models and practical algorithms for analysis and destabilizing terrorist networks. We present SocialAction, which we believe is more than just a YASNAT (“yet another social network analysis tool”) because it balances systematic and flexible exploration. To help users systematically examine measures, SocialAction applies attribute ranking and coordinated views to identify extreme-valued nodes (Section 3).
Anmerkungen

No source given. One gets the impression that iMiner possibly is a YASNAT.

Sichter
(Hindemith), WiseWoman

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