Open AccessProceedings Article
The PageRank Citation Ranking : Bringing Order to the Web
Lawrence Page,Sergey Brin,Rajeev Motwani,Terry Winograd +3 more
- Vol. 98, pp 161-172
TLDR
This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them, and shows how to efficiently compute PageRank for large numbers of pages.Abstract:
The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.read more
Citations
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Recommending trusted online auction sellers using social network analysis
Jyun-Cheng Wang,Chui-Chen Chiu +1 more
TL;DR: A recommendation system that uses trading relationships to calculate level of recommendation for trusted online auction sellers and can provide warning several months ahead of officially released blacklists to help guard against possible seller collusion is presented.
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GOTCHA! Network-Based Fraud Detection for Social Security Fraud
TL;DR: It is found that domain-driven network variables have a significant impact on detecting past and future frauds and improve the baseline by detecting up to 55% additional fraudsters over time.
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Learning from Collective Intelligence: Feature Learning Using Social Images and Tags
TL;DR: This article proposes a novel deep feature learning paradigm based on social collective intelligence, which can be acquired from the inexhaustible social multimedia content on the Web, in particular, largely social images and tags, and offers an easy-to-use implementation.
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The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics
TL;DR: The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC, and the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance.
Proceedings ArticleDOI
Characterizing data analysis workloads in data centers
TL;DR: The study on the workloads reveals that data analysis applications share many inherent characteristics, which place them in a different class from desktop, HPC, and service workloads, including traditional server workloads (SPECweb200S) and scale-out service workloadS (four among six benchmarks in CloudSuite), and accordingly the authors give several recommendations for architecture and system optimizations.
References
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Journal Article
The Anatomy of a Large-Scale Hypertextual Web Search Engine.
Sergey Brin,Lawrence Page +1 more
TL;DR: Google as discussed by the authors is a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.
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Efficient crawling through URL ordering
TL;DR: In this paper, the authors study in what order a crawler should visit the URLs it has seen, in order to obtain more "important" pages first, and they show that a good ordering scheme can obtain important pages significantly faster than one without.
Proceedings ArticleDOI
Silk from a sow's ear: extracting usable structures from the Web
TL;DR: This paper presents the exploration into techniques that utilize both the topology and textual similarity between items as well as usage data collected by servers and page meta-information lke title and size.
Proceedings ArticleDOI
HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
TL;DR: Experience with HyPursuit suggests that abstraction functions based on hypertext clustering can be used to construct meaningful and scalable cluster hierarchies, and is encouraged by preliminary results on clustering based on both document contents and hyperlink structures.
Journal ArticleDOI
The quest for correct information on the Web: hyper search engines
TL;DR: This paper presents a novel method to extract from a web object its “hyper” informative content, in contrast with current search engines, which only deal with the “textual’ informative content.