Stability and Generalization of Bipartite Ranking Algorithms

2005 ◽  
pp. 32-47 ◽  
Author(s):  
Shivani Agarwal ◽  
Partha Niyogi
Keyword(s):  
Ranking Algorithms ◽  
Bipartite Ranking

Strong and weak stability of bipartite ranking algorithms

2013 ◽  
Author(s):  
Wei Gao ◽  
Yungang Zhang ◽  
Yun Gao ◽  
Li Liang ◽  
Youming Xia
Keyword(s):  
Weak Stability ◽  
Ranking Algorithms ◽  
Bipartite Ranking

Network search

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Web Search ◽  
Distributed Databases ◽  
Small World ◽  
Peer Networks ◽  
Model Parameters ◽  
Breadth First Search ◽  
Ranking Algorithms ◽  
Network Search ◽  
Peer To Peer Networks ◽  
Search Processes

This chapter gives a discussion of search processes on networks. It begins with a discussion of web search, including crawlers and web ranking algorithms such as PageRank. Search in distributed databases such as peer-to-peer networks is also discussed, including simple breadth-first search style algorithms and more advanced “supernode” approaches. Finally, network navigation is discussed at some length, motivated by consideration of Milgram's letter passing experiment. Kleinberg's variant of the small-world model is introduced and it is shown that efficient navigation is possible only for certain values of the model parameters. Similar results are also derived for the hierarchical model of Watts et al.

scholarly journals Source identification of infectious diseases in networks via label ranking

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245344
Author(s):  
Jianye Zhou ◽  
Yuewen Jiang ◽  
Biqing Huang
Keyword(s):  
Infectious Diseases ◽  
Real World ◽  
Source Identification ◽  
Human Society ◽  
Practical Applications ◽  
Ranking Algorithms ◽  
Label Ranking ◽  
Infection Source ◽  
And Control ◽  
Complete Observation

Background Outbreaks of infectious diseases would cause great losses to the human society. Source identification in networks has drawn considerable interest in order to understand and control the infectious disease propagation processes. Unsatisfactory accuracy and high time complexity are major obstacles to practical applications under various real-world situations for existing source identification algorithms. Methods This study attempts to measure the possibility for nodes to become the infection source through label ranking. A unified Label Ranking framework for source identification with complete observation and snapshot is proposed. Firstly, a basic label ranking algorithm with complete observation of the network considering both infected and uninfected nodes is designed. Our inferred infection source node with the highest label ranking tends to have more infected nodes surrounding it, which makes it likely to be in the center of infection subgraph and far from the uninfected frontier. A two-stage algorithm for source identification via semi-supervised learning and label ranking is further proposed to address the source identification issue with snapshot. Results Extensive experiments are conducted on both synthetic and real-world network datasets. It turns out that the proposed label ranking algorithms are capable of identifying the propagation source under different situations fairly accurately with acceptable computational complexity without knowing the underlying model of infection propagation. Conclusions The effectiveness and efficiency of the label ranking algorithms proposed in this study make them be of practical value for infection source identification.

scholarly journals Approaches of Page Ranking Algorithms: Review

2013 ◽  
Vol 82 (2) ◽  
pp. 31-38
Author(s):  
Nripendra NarayanDas ◽  
Ela Kumar ◽  
Sheetal Sheetal
Keyword(s):  
Ranking Algorithms ◽  
Page Ranking

scholarly journals Stability and Similarity of Link Analysis Ranking Algorithms

2006 ◽  
Vol 3 (4) ◽  
pp. 479-507 ◽  
Author(s):  
Debora Donato ◽  
Stefano Leonardi ◽  
Panayiotis Tsaparas
Keyword(s):  
Link Analysis ◽  
Ranking Algorithms

MapReduce Based Information Retrieval Algorithms for Efficient Ranking of Webpages

2013 ◽  
pp. 250-265
Author(s):  
K.G. Srinivasa ◽  
Anil Kumar Muppalla ◽  
Varun A. Bharghava ◽  
M. Amulya
Keyword(s):  
Search Engines ◽  
World Wide ◽  
Speed Of Convergence ◽  
User Choice ◽  
Link Structure ◽  
Ranking Algorithms ◽  
The World ◽  
Retrieval Algorithms ◽  
Web Graph ◽  
The Web

In this paper, the authors discuss the MapReduce implementation of crawler, indexer and ranking algorithms in search engines. The proposed algorithms are used in search engines to retrieve results from the World Wide Web. A crawler and an indexer in a MapReduce environment are used to improve the speed of crawling and indexing. The proposed ranking algorithm is an iterative method that makes use of the link structure of the Web and is developed using MapReduce framework to improve the speed of convergence of ranking the WebPages. Categorization is used to retrieve and order the results according to the user choice to personalize the search. A new score is introduced in this paper that is associated with each WebPage and is calculated using user’s query and number of occurrences of the terms in the query in the document corpus. The experiments are conducted on Web graph datasets and the results are compared with the serial versions of crawler, indexer and ranking algorithms.

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