View - Observatoire Astronomique de Strasbourg

More documents

Recommendations

Info

Working provisional copy - Pls do not circulate 28 MICHAEL J. KURTZ applied the method to stellar spectra, and Connolly, et al (1995) rediscovered it for galaxy spectra. While the methods have been used, they have not found great success, compared to partitioning line ratio diagrams (Baldwin, et al 1981) or color-magnitude diagrams (Golay, et al 1977). This is likely because, while the techniques are linear, the underlying physics is highly non-linear (Kurtz 1982). Human thought is also highly non-linear; representing it by a linear vector space is bound to cause problems. Although the success of LSA as an indexing method for text demonstrated that very local measures of nearness can be effectively defined, more generally these are not metric spaces. As an example, human perceptions do not follow the Schwartz or triangle inequality. The perceptual distance between a dog and a vacuum cleaner is made shorter by the introduction of a third point, a mechanical dog; a mechanical dog is a kind of dog, and a mechanical dog is a kind of vacuum cleaner (Kurtz 1989). 4.1.2. Social Network Techniques By analyzing the connections between entities (people, documents, molecules, traffic jams, ...) one can build powerful descriptive and predictive models (Barabasi 2003, Newman 2003). Many of the techniques were developed for the social sciences (Wassermann and Faust 1994) and are now widespread. Fifteen percent of the papers published in 2009 by Physical Review E address aspects of network problems, as do twenty-six percent of papers published in PLOS Computational Biology, for example. The structure of information networks contains information about the intelligent processes which created them. Obtaining the underlying structure from the network is called community detection, and is similar to techniques for cluster analysis or classification. Fortunato (2010) reviews these methods, currently the most popular method is that of Girvan and Newman (2002) and the “best” (according to Lancichinetti and Fortunato 2009) is that of Rosvall and Bergstrom (RB: 2008). RB have used their algorithm on citation data to show the interrelationships between the major fields of science; their map may very profitably be compared with the similar map of Bolen et al (2009a) who show a similar structure based on usage data and pre-existing field classifications. The RB algorithm has been used by Kurtz, et al (2007) and Henneken et al (2009) to map the subfields of astronomy, based on both citation data and on shared keywords for journal articles. Attempts to build a similar map from usage data have not, thus far, been successful, perhaps because of the very broad readership patterns of many astronomers. Another measure obtainable from a network graph is the “importance” of the individual nodes. Importance is normally called centrality in this
The Emerging Scholarly Brain 29 context, and there are several different centrality measures. In a friendship network, where people are the nodes and they are linked to other people by friendship, the person with the most friends would be the person with the highest degree centrality. Note that friendship is directional, I may consider you my friend, but that does not mean that you consider me a friend; thus the concepts of in-degree and out-degree. In a citation network the paper with the highest in-degree is the most cited paper, while review articles would have very high out-degree. Betweenness centrality is another “importance” measure. In a friendship network the most central people are those with friends in many different, otherwise autonomous cliques; in a journal to journal citation network the most central journals are the interdisciplinary journals (Leydesdorff 2007), like Science or Nature, which are between otherwise autonomous fields, such as astronomy and neuroscience. Betweenness centrality is a key measure in an information flow network; the high betweeness centrality nodes facilitate information transfer between fields. The currently most used centrality measure is the expected occupation time for each node when visited by a random walk, where the agent randomly follows links from node to node. This is normally called eigenvector centrality, as the result is the same as the first eigenvector of the nodenode connectivity matrix (Bonacich 1971). Google’s famous Page-Rank algorithm (Brin and Page 1998) is essentially eigenvector centrality, with clever implementation details. There are several other centrality measures. Kurtz and Bollen (2010) give a brief introduction; Koschützki, et al (2005) a detailed discussion. 4.2. LOCAL MEASURES Working provisional copy - Pls do not circulate While measures which solve for globally optimum measures are clearly desirable and useful, they are often not feasible. This is especially true in situations where the data is inherently highly multipartite. Journal articles, for example, may be connected by citations, but they may also be connected by having the same author, or having been read by the same reader, or concerning the same subject matter, or the same astronomical object, or .... An interesting question is how much is lost if one uses local measures, instead of attempting a global solution. Some recent work discusses this. Eigenfactor (West, et al 2010a) is a useful measure of scholarly journals; it is available in the Thompson-Reuters Web of Science, and finds frequent use by librarians in making purchasing decisions. The Eigenfactor is essentially eigenvector centrality, measured on the journal to journal citation graph.
Page 1: Harvard-Smithsonian Center for Astr
Page 4 and 5: Working provisional copy - Pls do n
Page 8 and 9: iv Working provisional copy - Pls d
Page 10 and 11: Bohlen, Elizabeth SAO MS-83 60 Gard
Page 12 and 13: Jorda, Jean-Paul EDP Sciences 17, a
Page 14 and 15: Thompson, Donna SAO MS-83 60 Garden
Page 18 and 19: 2 ANDRÉ HECK cific books on inform
Page 20 and 21: 4 ANDRÉ HECK Figure 2. A schematic
Page 22 and 23: 6 ANDRÉ HECK Figure 3. ERA archive
Page 24 and 25: 8 ANDRÉ HECK In a similar approach
Page 26 and 27: 10 ANDRÉ HECK Acknowledgements I a
Page 28 and 29: 12 JOHN HUCHRA Working provisional
Page 38 and 39: 22 JOHN HUCHRA major research unive
Page 42 and 43: 26 MICHAEL J. KURTZ 3. Architecture
Page 60 and 61: 44 RICHARD TRESCH FIENBERG Laborato
Page 62 and 63: 46 RICHARD TRESCH FIENBERG − Conv
Page 64 and 65: 48 GUENTHER EICHHORN Figure 1. Deve
Page 68 and 69: 52 GUENTHER EICHHORN 5. Exemplar Ex
Page 72 and 73: 56 GUENTHER EICHHORN The changes in
Page 78 and 79: 62 MARIE-LOUISE CHAIX Royal Academy
Page 86 and 87: 70 JOSEPH B. JENSEN ET AL. munity,
Page 94 and 95:
Working provisional copy - Pls do n
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
84 HELMUT A. ABT Figure 1. (Top) Th
Page 102 and 103:
86 HELMUT A. ABT Figure 3. The left
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
92 CHRIS BIEMESDERFER digital mater
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
102 KEVIN B. MARVEL AND CHRIS BIEME
Page 120 and 121:
104 KEVIN B. MARVEL AND CHRIS BIEME
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
112 JILL LAGERSTROM AND UTA GROTHKO
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
120 UTA GROTHKOPF AND JILL LAGERSTR
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
132 TERENCE J. MAHONEY The way in w
Page 150 and 151:
134 TERENCE J. MAHONEY Cannon, Anto
Page 152 and 153:
Page 154 and 155:
138 EDWIN A. HENNEKEN ET AL. Figure
Page 156 and 157:
140 EDWIN A. HENNEKEN ET AL. 2. Rec
Page 158 and 159:
142 EDWIN A. HENNEKEN ET AL. profes
Page 160 and 161:
144 EDWIN A. HENNEKEN ET AL. Figure
Page 162 and 163:
146 EDWIN A. HENNEKEN ET AL. The
Page 164 and 165:
Page 166 and 167:
150 ALBERTO ACCOMAZZI verify that a
Page 168 and 169:
152 ALBERTO ACCOMAZZI 4. A Way Forw
Page 170 and 171:
154 ALBERTO ACCOMAZZI keywords, and
Page 172 and 173:
Page 174 and 175:
158 MICHAEL J. KURTZ Recognition, V
Page 176 and 177:
Page 178:
show all

View - Observatoire Astronomique de Strasbourg

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?