Techniques for Disaggregating Centrality Scores in Social Networks

Mizruchi, Mark S.; Mariolis, Peter; Schwartz, Michael; Mintz, Beth

doi:10.2307/270918

Cited by 41 publications

(22 citation statements)

References 12 publications

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“…A different design is conceivable in which keyword search is used to locate an initial set of pages (using a giant crawl and index), expand this graph to a limited radius and then look for popular sites in the expanded graph using weighted degree measures [25,31,4,21,6,3]. This approach was tried as a semiautomatic means to build a taxonomy like Yahoo!.…”

Section: Justification and Discussionmentioning

confidence: 99%

“…Mizruchi et al [25] recognized that centrality in a social network can be disaggregated into derived and reflected centrality. They found two types of nodes: bridges which have high derived centrality, and hubs which link with good authorities and thereby have high reflected centrality.…”

Section: Distillationmentioning

confidence: 99%

“…Social networks have been analyzed for decades to find nodes with high prestige and reflected prestige [20,25,31]. Similar to PageRank [4], HITS [21], CLEVER [6], topic distillation [3] and link-based similarity search [13], we use social network analysis as a subroutine in our system.…”

Section: Related Workmentioning

confidence: 99%

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Focused crawling: a new approach to topic-specific Web resource discovery

1999

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The rapid growth of the World-Wide Web poses unprecedented scaling challenges for general-purpose crawlers and search engines. In this paper we describe a new hypertext resource discovery system called a Focused Crawler. The goal of a focused crawler is to selectively seek out pages that are relevant to a pre-defined set of topics. The topics are specified not using keywords, but using exemplary documents. Rather than collecting and indexing all accessible Web documents to be able to answer all possible ad-hoc queries, a focused crawler analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and avoids irrelevant regions of the Web. This leads to significant savings in hardware and network resources, and helps keep the crawl more up-to-date.To achieve such goal-directed crawling, we designed two hypertext mining programs that guide our crawler: a classifier that evaluates the relevance of a hypertext document with respect to the focus topics, and a distiller that identifies hypertext nodes that are great access points to many relevant pages within a few links. We report on extensive focused-crawling experiments using several topics at different levels of specificity. Focused crawling acquires relevant pages steadily while standard crawling quickly loses its way, even though they are started from the same root set. Focused crawling is robust against large perturbations in the starting set of URLs. It discovers largely overlapping sets of resources in spite of these perturbations. It is also capable of exploring out and discovering valuable resources that are dozens of links away from the start set, while carefully pruning the millions of pages that may lie within this same radius. Our anecdotes suggest that focused crawling is very effective for building high-quality collections of Web documents on specific topics, using modest desktop hardware.

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Section: Justification and Discussionmentioning

confidence: 99%

Section: Distillationmentioning

confidence: 99%

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Focused crawling: a new approach to topic-specific Web resource discovery

1999

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“…First there are those who view a person as central in a social network to the extent that he or she is somehow close to everyone else in the network (Bavelas 1950;Katz 1953;Shaw 1954;Harary 1959;Faucheux and Moscovici 1960;Garrison 1960;Beauchamp 1965;Pitts 1965;Hubbell 1965;Mackenzie 1966;Sabidussi 1966;Bonacich 1972Bonacich , 1987Coleman 1973;Nieminen 1973Nieminen , 1974Moxley and Moxley 1974;Rogers 1974;Czepiel 1974;Kajitani and Maruyama 1976;Burt 1982;Mizruchi, Mariolis, Schwartz and Mintz 1986;Stephenson and Zelen 1989;Friedkin 1991).…”

Section: Introductionmentioning

confidence: 99%

Centrality in valued graphs: A measure of betweenness based on network flow

1991

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“…There is a very rich literature of methods for square, binary sociomatrices. Much of this literature has appeared in the serial Sociological Methodology (Holland and Leinhardt 1975;Schwartz 1976 Mizruchi et al 1986). It will be convenient to view these methods as basically of three types: graph-theoretic (Harary, Norman, and Cartwright 1965), relational or algebraic (see Wu 1983 and Winship and Mandel 1983 for thorough reviews), and statistical (e.g., Holland and Leinhardt 1975;Fienberg and Wasserman 1981; see also the review by Frank 1981).…”

Section: Network Datamentioning

confidence: 99%