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Titlebook: Algorithms and Models for the Web-Graph; Fourth International William Aiello,Andrei Broder,Evangelos Milios Conference proceedings 2008 Spr

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樓主: Agitated
31#
發(fā)表于 2025-3-26 23:01:48 | 只看該作者
Expansion and Lack Thereof in Randomly Perturbed Graphs,d?s-Rényi graph to an arbitrary connected graph..The central results show that there exists a constant . such that when any connected .-vertex base graph . is perturbed by adding a random 1-out then, with high probability, the resulting graph has . for all .???. with .. When . is perturbed by adding
32#
發(fā)表于 2025-3-27 04:50:10 | 只看該作者
Web Structure in 2005,rver based on the results of three previous studies, 200 pages, by the estimated number of web servers on the Internet, 101.4 million. However, based on the analysis of 8.5 billion web pages that we crawled by Oct. 2005, we estimate the total number of web pages to be 53.7 billion. This is because t
33#
發(fā)表于 2025-3-27 05:33:17 | 只看該作者
34#
發(fā)表于 2025-3-27 11:55:45 | 只看該作者
35#
發(fā)表于 2025-3-27 17:15:30 | 只看該作者
36#
發(fā)表于 2025-3-27 20:20:07 | 只看該作者
Communities in Large Networks: Identification and Ranking,ives of the community. We define the concept of a . justified by a formal analysis of a simple model of the evolution of a directed graph. We show that the problem of deciding whether a non trivial community exists is NP complete. Nevertheless, experiments show that a very simple greedy approach can
37#
發(fā)表于 2025-3-27 23:51:08 | 只看該作者
38#
發(fā)表于 2025-3-28 04:51:22 | 只看該作者
Traps and Pitfalls of Topic-Biased PageRank,ten with contradictory approaches. We study the difference between . and . preferential PageRank, which patch the dangling nodes with different distributions, extending analytical formulae known for the strongly preferential case, and corroborating our results with experiments on a snapshot of 100 m
39#
發(fā)表于 2025-3-28 09:23:50 | 只看該作者
40#
發(fā)表于 2025-3-28 14:06:55 | 只看該作者
A Phrase Recommendation Algorithm Based on Query Stream Mining in Web Search Engines,s are extracted from past user queries based on the frequency rate of the phrases. A query recommender algorithm called OQD (Online Query Discovery) has also been designed for comparison purposes. Simulation results show the efficiency of the proposed phrase recommender algorithm compared to the OQD
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