Network search with inadmissible heuristics

2005 ◽  
pp. 439-450
Author(s):  
A. Mahanti ◽  
K. Ray
Keyword(s):  
Network Search

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 Heuristic rank selection with progressively searching tensor ring network

2021 ◽  
Author(s):  
Nannan Li ◽  
Yu Pan ◽  
Yaran Chen ◽  
Zixiang Ding ◽  
Dongbin Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Compression Ratio ◽  
State Of The Art ◽  
Heuristic Method ◽  
Ring Networks ◽  
Ring Network ◽  
Narrow Region ◽  
Network Search ◽  
Deep Networks ◽  
Evolutionary Phase

AbstractRecently, tensor ring networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performance of TRNs, rank selection is seldom studied in previous works and usually set to equal in experiments. Meanwhile, there is not any heuristic method to choose the rank, and an enumerating way to find appropriate rank is extremely time-consuming. Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region. Therefore, based on the above phenomenon, we propose a novel progressive genetic algorithm named progressively searching tensor ring network search (PSTRN), which has the ability to find optimal rank precisely and efficiently. Through the evolutionary phase and progressive phase, PSTRN can converge to the interest region quickly and harvest good performance. Experimental results show that PSTRN can significantly reduce the complexity of seeking rank, compared with the enumerating method. Furthermore, our method is validated on public benchmarks like MNIST, CIFAR10/100, UCF11 and HMDB51, achieving the state-of-the-art performance.

scholarly journals Predictive Modelling and Surface Analysis for Optimization of Production of Biofuel as A Renewable Energy Resource: Proposition of Artificial Neural Network Search

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Sri Krishna Murthy ◽  
Ankit Goyal ◽  
N. Rajasekar ◽  
Kapil Pareek ◽  
Thoi Trung Nguyen ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Ethyl Ester ◽  
Global Sensitivity Analysis ◽  
Catalyst Concentration ◽  
Energy Resource ◽  
Predictive Modelling ◽  
Renewable Energy Resource ◽  
Network Search ◽  
Ester Yield ◽  
Time Of Reaction

The present study undertakes the research problem on the optimization of production of biodiesel as a renewable energy resource from the transesterification of soybean oil and ethanol. Predictive modelling and surface analysis techniques were applied based on the artificial neural network search algorithm to correlate the yield of ethyl ester and glycerol and the input parameters. The formulated models accurately predicted the yield of the products with a high coefficient of determination. When the reaction time is low, the ester yield decreases with an increase in temperature and the maximum yield of obtained biodiesel at a very low value of time of reaction and temperature. Plots based on parametric and sensitivity analysis reveals that the yield of ethyl ester can be maximized and that of glycerol minimized at an integrated condition with lower ethanol/oil molar ratio, higher temperature value, higher catalyst concentration value, and longer time of reaction. The global sensitivity analysis reveals that the catalyst concentration and temperature of the reaction influence the yield of ethyl ester the most. In addition, an optimal ethyl ester yield of 95% can be achieved at specific input conditions. Moreover, according to the results of global sensitivity analysis, the catalyst concentration is found to be most significant for both the glycerol and ethyl ester yield.

Algorithms for Regular Tree Grammar Network Search and Their Application to Mining Human-Viral Infection Patterns

2015 ◽  
pp. 53-65
Author(s):  
Ilan Smoly ◽  
Amir Carmel ◽  
Yonat Shemer-Avni ◽  
Esti Yeger-Lotem ◽  
Michal Ziv-Ukelson
Keyword(s):  
Viral Infection ◽  
Regular Tree ◽  
Tree Grammar ◽  
Network Search

scholarly journals NETWORK SEARCH: CLIMBING THE JOB LADDER FASTER

2018 ◽  
Vol 60 (2) ◽  
pp. 693-720 ◽  
Author(s):  
Marcelo Arbex ◽  
Dennis O'Dea ◽  
David Wiczer
Keyword(s):  
Network Search

scholarly journals Private algorithms for the protected in social network search

2016 ◽  
Vol 113 (4) ◽  
pp. 913-918 ◽  
Author(s):  
Michael Kearns ◽  
Aaron Roth ◽  
Zhiwei Steven Wu ◽  
Grigory Yaroslavtsev
Keyword(s):  
Infectious Disease ◽  
Social Networks ◽  
Social Network ◽  
Data Privacy ◽  
Large Scale ◽  
Graph Search ◽  
Medical Testing ◽  
Background Checks ◽  
Network Search ◽  
Natural Variants

Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.

scholarly journals Network Search from a Game Theoretic Perspective

2013 ◽  
pp. 60-70
Author(s):  
Steve Alpern
Keyword(s):  
Network Search ◽  
Game Theoretic

scholarly journals Crude Oil Price Fluctuation Analysis Under Considering Emergency and Network Search Data

2020 ◽  
Vol 4 (12) ◽  
pp. 2000051
Author(s):  
Wan‐qiang Dai ◽  
Wei Pan ◽  
Yongdong Shi ◽  
Cheng Hu ◽  
Wulin Pan ◽  
...  
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Fluctuation Analysis ◽  
Crude Oil Price ◽  
Price Fluctuation ◽  
Network Search ◽  
Search Data ◽  
Oil Price Fluctuation
Sign in / Sign up

Export Citation Format

Share Document