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Priority Algorithms for Graph Optimization Problems [chapter]

Allan Borodin, Joan Boyar, Kim S. Larsen
2005 Lecture Notes in Computer Science  
We study a variety 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  ... 
doi:10.1007/978-3-540-31833-0_12 fatcat:7eed2ajfhjgnhfczv6aqpuqhce

Priority algorithms for graph optimization problems

Allan Borodin, Joan Boyar, Kim S. Larsen, Nazanin Mirmohammadi
2010 Theoretical Computer Science  
We study a variety 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  ... 
doi:10.1016/j.tcs.2009.09.033 fatcat:ohz4f5jy65gsvapew2adzhcese

Efficient influence maximization in social networks

Wei Chen, Yajun Wang, Siyu Yang
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  ... 
doi:10.1145/1557019.1557047 dblp:conf/kdd/ChenWY09 fatcat:ejvr3f4zr5drjcwxqbs42l3dr4

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

Padraig Corcoran, Andrei Gagarin
2021 Computers & Operations Research  
The k-domination 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.  ... 
doi:10.1016/j.cor.2021.105368 fatcat:tsgyfqek65df3mwdzvz2qgnt2m

Graph Neural Network based scheduling : Improved throughput under a generalized interference model [article]

S. Ramakrishnan, Jaswanthi Mandalapu, Subrahmanya Swamy Peruru, Bhavesh Jain, Eitan Altman
2021 arXiv   pre-print
In particular, we consider a generalized interference 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.  ... 
arXiv:2111.00459v1 fatcat:cenr7jcnwvamvpchu3kwldh24a

Construction of Minimum-Weight Spanners [chapter]

Mikkel Sigurd, Martin Zachariasen
2004 Lecture Notes in Computer Science  
By using the solutions (and lower bounds) from this 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.  ... 
doi:10.1007/978-3-540-30140-0_70 fatcat:n357bg47gbdtdlliejhftd5yd4

A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network

Haojian Liang, Shaohua Wang, Huilai Li, Huichun Ye, Yang Zhong
2022 ISPRS International Journal of Geo-Information  
We propose a new paradigm that combines a 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].  ... 
doi:10.3390/ijgi11050270 fatcat:cx2g5el4kzacfb3fbjwzpmlexm

Integer programming formulations for the minimum weighted maximal matching problem

Z. Caner Taşkın, Tınaz Ekim
2011 Optimization Letters  
Most 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).  ... 
doi:10.1007/s11590-011-0351-x fatcat:lgosbtis5ng7xbf6mcj5ytjw3a

Estimate on Expectation for Influence Maximization in Social Networks [chapter]

Yao Zhang, Qing Gu, Jun Zheng, Daoxu Chen
2010 Lecture Notes in Computer Science  
Based on the approximation to the expectation, we put forward a new 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.  ... 
doi:10.1007/978-3-642-13657-3_13 fatcat:i4imtezq7vahplwhjozprbmfa4

Some recent results in the analysis of greedy algorithms for assignment problems

Ulrich Faigle
1994 OR spectrum  
We survey some recent developments in the analysis 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.  ... 
doi:10.1007/bf01719448 fatcat:sry5lokypvhkrnijxculcemzge

Greedy learning of graphical models with small girth

Avik Ray, Sujay Sanghavi, Sanjay Shakkottai
2012 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)  
This paper develops two new 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.  ... 
doi:10.1109/allerton.2012.6483471 dblp:conf/allerton/RaySS12 fatcat:jgca4k4ownghhmvsh6f5o735me

Recommendation Subgraphs for Web Discovery [article]

Arda Antikacioglu, R. Ravi, Srinath Srihdar
2014 arXiv   pre-print
This motivated us to analyze three methods 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.  ... 
arXiv:1409.2042v1 fatcat:43duhly6w5cebdffoot5rp2pz4

Scalable Influence Maximization in Social Networks under the Linear Threshold Model

Wei Chen, Yifei Yuan, Li Zhang
2010 2010 IEEE International Conference on Data Mining  
We conduct extensive simulations to show that our 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%.  ... 
doi:10.1109/icdm.2010.118 dblp:conf/icdm/ChenYZ10 fatcat:kx3u2zmgxbedjoxq7b2sf2z7e4

Influence Spread Maximization in Social Network

Xinfei Shi, Hongzhi Wang, Jianzhong Li, Hong Gao
2013 International Journal of Information and Education Technology  
Also we transform this 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.  ... 
doi:10.7763/ijiet.2013.v3.357 fatcat:k22cpu3ahbfqndj6yyj5mx3bvu

Matching through Embedding in Dense Graphs [article]

Nitish K. Panigrahy, Prithwish Basu, Don Towsley
2020 arXiv   pre-print
While developing optimal solutions 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  ... 
arXiv:2011.06767v1 fatcat:hfgbx3dmcrg7beqdwf7yxy3bue
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