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Priority Algorithms for Graph Optimization Problems
[chapter]

2005
*
Lecture Notes in Computer Science
*

We study a variety

doi:10.1007/978-3-540-31833-0_12
fatcat:7eed2ajfhjgnhfczv6aqpuqhce
*of**graph**problems*in the context*of*arbitrary and restricted priority*models*corresponding to known "*greedy**algorithms*". ... We continue the study*of*priority or "*greedy*-like"*algorithms*as initiated in [6] and as extended to*graph*theoretic*problems*in [8] . ... In this paper, we continue the study*of*priority*algorithms**for**graph**problems*using two*models*(again motivated by current*algorithms*), namely the vertex adjacency*model*as in Davis and Impagliazzo and ...##
###
Priority algorithms for graph optimization problems

2010
*
Theoretical Computer Science
*

We study a variety

doi:10.1016/j.tcs.2009.09.033
fatcat:ohz4f5jy65gsvapew2adzhcese
*of**graph**problems*in the context*of*arbitrary and restricted priority*models*corresponding to known "*greedy**algorithms*". ... We continue the study*of*priority or "*greedy*-like"*algorithms*as initiated in [6] and as extended to*graph*theoretic*problems*in [8] . ... In this paper, we continue the study*of*priority*algorithms**for**graph**problems*using two*models*(again motivated by current*algorithms*), namely the vertex adjacency*model*as in Davis and Impagliazzo and ...##
###
Efficient influence maximization in social networks

2009
*
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
*

*algorithm*, and more importantly (c) the degree discount heuristics run only in milliseconds while even the improved

*greedy*

*algorithms*run in hours in our experiment

*graphs*with a few tens

*of*thousands ... achieve much better influence spread than classic degree and centrality-based heuristics, and when tuned

*for*a specific influence cascade

*model*, it achieves almost matching influence thread with the

*greedy*...

*Problem*definition and the

*greedy*

*algorithm*A social network is

*modeled*as an undirected

*graph*G = (V, E), with vertices in V

*modeling*the individuals in the network and edges in E

*modeling*the relationship ...

##
###
Heuristics for k-domination models of facility location problems in street networks

2021
*
Computers & Operations Research
*

The k-domination

doi:10.1016/j.cor.2021.105368
fatcat:tsgyfqek65df3mwdzvz2qgnt2m
*problem*with respect to this class*of**graphs*can be used to*model*real-world facility location*problem*scenarios. ... We present new*greedy*and beam search heuristic methods to find small-size k-dominating sets in*graphs*. ...*For*example, the*problem**of*finding a shortest path in a street network can be*modelled*as the*problem**of*computing the shortest path in a*graph*which*models*that street network. ...##
###
Graph Neural Network based scheduling : Improved throughput under a generalized interference model
[article]

2021
*
arXiv
*
pre-print

In particular, we consider a generalized interference

arXiv:2111.00459v1
fatcat:cenr7jcnwvamvpchu3kwldh24a
*model*called the k-tolerant conflict*graph**model*and design an efficient approximation*for*the well-known Max-Weight scheduling*algorithm*. ... In this work, we propose a*Graph*Convolutional Neural Networks (GCN) based scheduling*algorithm**for*adhoc networks. ... The authors are grateful to the OPAL infrastructure from Université Côte d'Azur*for*providing resources and support. This paper is accepted in EAI VALUETOOLS 2021. ...##
###
Construction of Minimum-Weight Spanners
[chapter]

2004
*
Lecture Notes in Computer Science
*

By using the solutions (and lower bounds) from this

doi:10.1007/978-3-540-30140-0_70
fatcat:n357bg47gbdtdlliejhftd5yd4
*algorithm*, we experimentally evaluate the performance*of*the*greedy**algorithm**for*a set*of*realistic*problem*instances. ... These applications use some variant*of*the socalled*greedy**algorithm**for*constructing the spanner -an*algorithm*that mimics Kruskal's minimum spanning tree*algorithm*. ... Most*of*these are obtained by (variants*of*) the*greedy**algorithm*. The performance*of*the*greedy**algorithm*depends on the weight function on the edges*of*G.*For*general*graphs*, Althöfer et al. ...##
###
A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network

2022
*
ISPRS International Journal of Geo-Information
*

We propose a new paradigm that combines a

doi:10.3390/ijgi11050270
fatcat:cx2g5el4kzacfb3fbjwzpmlexm
*graph*convolution network and*greedy**algorithm*to solve the p-center*problem*through direct training and realize that the efficiency is faster than the exact*algorithm*... The accuracy is superior to the heuristic*algorithm*. We generate a large amount*of*p-center*problems*by the Erdos–Renyi*graph*, which can generate instances in many real*problems*. ... Geo-Inf. 2022, 11, 270 5*of*15 λ ≤ η(n), where η(n)*Algorithm*2 2*Greedy**Algorithm**for*the PC*problem*(GA) ISPRS Int. J. Geo-Inf. 2022, 11, x*FOR*PEER REVIEW 8*of*Lemma [34]. ...##
###
Integer programming formulations for the minimum weighted maximal matching problem

2011
*
Optimization Letters
*

Most

doi:10.1007/s11590-011-0351-x
fatcat:lgosbtis5ng7xbf6mcj5ytjw3a
*of*the existing literature considers the*problem*in some restricted classes*of**graphs*and give polynomial time exact or approximation*algorithms*. ... Given an undirected*graph*, the*problem**of*finding a maximal matching that has minimum total weight is NP-hard. This*problem*has been studied extensively from a*graph*theoretical point*of*view. ... [4] 's*greedy**algorithm**for*MMM, which is known to have an approximation ratio*of*2 (and strictly less than 2*for*dense*graphs*). ...##
###
Estimate on Expectation for Influence Maximization in Social Networks
[chapter]

2010
*
Lecture Notes in Computer Science
*

Based on the approximation to the expectation, we put forward a new

doi:10.1007/978-3-642-13657-3_13
fatcat:i4imtezq7vahplwhjozprbmfa4
*greedy**algorithm*called*Greedy*Estimate-Expectation (GEE), whose advantage over the previous*algorithm*is to estimate marginal gains ... We formulate the expectation*of*the influence function and its marginal gain first, then give bounds to the expectation*of*marginal gains. ... (a) IC*model**for*Gr-Qc*graph*. (b) IC*model**for*Hep*graph*. (c) WC*model**for*Gr-Qc*graph*. (d) WC*model**for*Hep*graph*. Fig. 2 . 2 Running time(sec.). (a) Gr-Qc*graph*. (b) Hep*graph*. ...##
###
Some recent results in the analysis of greedy algorithms for assignment problems

1994
*
OR spectrum
*

We survey some recent developments in the analysis

doi:10.1007/bf01719448
fatcat:sry5lokypvhkrnijxculcemzge
*of**greedy**algorithms**for*assignment and transportation*problems*. ... , and on-line*algorithms**for*linear and non-linear assignment*problems*. ...*For*the*problem**of*finding an optimal weighted spanning tree in a*graph*, it also goes under the name*of*Kruskal's*algorithm*. ...##
###
Greedy learning of graphical models with small girth

2012
*
2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
*

This paper develops two new

doi:10.1109/allerton.2012.6483471
dblp:conf/allerton/RaySS12
fatcat:jgca4k4ownghhmvsh6f5o735me
*greedy**algorithms**for*learning the Markov*graph**of*discrete probability distributions, from samples thereof. ... : (i) A recursive*greedy**algorithm*, and (ii) a forward-backward*greedy**algorithm*, that correctly learn the graphical*model*structure*for*... Moreover the*greedy**algorithms*we propose are applicable*for*a wider class*of*graphical*models*. ...##
###
Recommendation Subgraphs for Web Discovery
[article]

2014
*
arXiv
*
pre-print

This motivated us to analyze three methods

arXiv:1409.2042v1
fatcat:43duhly6w5cebdffoot5rp2pz4
*for*solving the*problem*in increasing order*of*sophistication: a sampling*algorithm*, a*greedy**algorithm*and a more involved partitioning based*algorithm*. ... We first theoretically analyze the performance*of*these three methods on random*graph**models*characterizing when each method will yield a solution*of*sufficient quality and the parameter ranges when more ... Acknowledgments: We thank Alan Frieze and Ashutosh Garg*for*helpful discussions. ...##
###
Scalable Influence Maximization in Social Networks under the Linear Threshold Model

2010
*
2010 IEEE International Conference on Data Mining
*

We conduct extensive simulations to show that our

doi:10.1109/icdm.2010.118
dblp:conf/icdm/ChenYZ10
fatcat:kx3u2zmgxbedjoxq7b2sf2z7e4
*algorithm*is scalable to networks with millions*of*nodes and edges, is orders*of*magnitude faster than the*greedy*approximation*algorithm*proposed by Kempe ... Based on the fast computation in DAGs, we propose the first scalable influence maximization*algorithm*tailored*for*the linear threshold*model*. ... show that the maximization*problem*in both*models*are NP-hard, and then they provide a*greedy*approximation*algorithm**for*both*models*that achieves an approximation ratio*of*63%. ...##
###
Influence Spread Maximization in Social Network

2013
*
International Journal of Information and Education Technology
*

Also we transform this

doi:10.7763/ijiet.2013.v3.357
fatcat:k22cpu3ahbfqndj6yyj5mx3bvu
*problem*to a reachable probability query*problem*in an uncertain*graph*; 2) we present a more accurate degree discount heuristic*algorithm*which considers the relationship between ... Intensive experiments on a large real-world social network show that: our improved*greedy**algorithm*and degree discount heuristic*algorithm*are more efficient than the basic*greedy**algorithm*and other ... (25 and 12 times) faster than the*greedy**algorithm*because we limit the calculation in a smaller region. 2) IC*model**For*IC*model*, we compare the methods in two aspects. ...##
###
Matching through Embedding in Dense Graphs
[article]

2020
*
arXiv
*
pre-print

While developing optimal solutions

arXiv:2011.06767v1
fatcat:hfgbx3dmcrg7beqdwf7yxy3bue
*for*various matching*problems*is important, the running times*of*the fastest available optimal matching*algorithms*are too costly. ... In this paper, we propose a novel network embedding based heuristic*algorithm*to solve various matching*problems*in dense*graphs*. ... The views and conclusions contained in this document are those*of*the authors and should not be interpreted as representing the official policies, either expressed or implied,*of*the National Science Foundation ...
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