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THIRTY YEARS OF GRAPH MATCHING IN PATTERN RECOGNITION
, 2004
"... A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented ..."
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Cited by 197 (1 self)
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A recent paper posed the question: "Graph Matching: What are we really talking about?". Far from providing a definite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition field. To this aim two taxonomies are presented and discussed. The first includes almost all the graph matching algorithms proposed from the late seventies, and describes the different classes of algorithms. The second taxonomy considers the types of common applications of graphbased techniques in the Pattern Recognition and Machine Vision field.
Scalable Maximum Clique Computation Using MapReduce
"... We present a scalable and faulttolerant solution for the maximum clique problem based on the MapReduce framework. Thekeycontributionthatenablesusto effectively use MapReduce is a recursive partitioning method that partitions the graph into several subgraphs of similar size. After partitioning, the ..."
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Cited by 5 (1 self)
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We present a scalable and faulttolerant solution for the maximum clique problem based on the MapReduce framework. Thekeycontributionthatenablesusto effectively use MapReduce is a recursive partitioning method that partitions the graph into several subgraphs of similar size. After partitioning, the maximum cliques of the different partitions can be computed independently, and the computation is sped up using a branch and bound method. Our experiments show that our approach leads to good scalability, which is unachievable by other partitioning methods since they result in partitions of different sizes and hence lead to load imbalance. Our method is more scalable than an MPI algorithm, and is simpler and more fault tolerant.
A penaltyevaporation heuristic in a decomposition method for the maximum clique problem
 IN OPTIMIZATION DAYS
, 2003
"... In this paper, we present a heuristic method to solve the maximum clique problem, based on the concepts of penalty and evaporation. At each iteration, some vertex i is inserted into the current solution (always a clique) and the vertices that are not adjacent to vertex i are removed from the solutio ..."
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In this paper, we present a heuristic method to solve the maximum clique problem, based on the concepts of penalty and evaporation. At each iteration, some vertex i is inserted into the current solution (always a clique) and the vertices that are not adjacent to vertex i are removed from the solution. The removed vertices are then penalized in order to reduce their potential of being selected to be inserted in the solution again during the next iterations. This penalty is gradually evaporating to allow vertices to become interesting subsequently. This penaltyevaporation heuristic method is embedded in a decomposition algorithm that restricts the search for a maximum clique to subgraphs, but performs an aggressive exploration of the feasible domain. Numerical results indicate that the penaltyevaporation heuristic method alone is effective and reliable, but the gain in quality obtained when embedding it in the decomposition algorithm is worthy of the additional computing time required.
Volunteer Computing Using Casual Games
"... We introduce the idea of volunteer computing gamesâ€“ that is, casual games which are implementations of distributed algorithms. Volunteer computing (VC) is a form of distributed computing which seeks to harness the computational power of individuals from around the world for free. Although the number ..."
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We introduce the idea of volunteer computing gamesâ€“ that is, casual games which are implementations of distributed algorithms. Volunteer computing (VC) is a form of distributed computing which seeks to harness the computational power of individuals from around the world for free. Although the numbers and types of VC projects has grown significantly over the past decade, the majority of participants in these projects are still from a limited demographic, and there are still many people who know nothing about these projects. On the other hand, most people know about casual games, and a majority of people play them. We propose that the use of casual gaming in volunteer computing projects can significantly increase participation, and therefore success, and we describe a prototype of a game that solves the maximum clique problem. 1
Article MultiThreading a StateoftheArt Maximum Clique Algorithm
, 2013
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Acknowledgements
, 2009
"... on climate change and the environmentTowards a Global Green Recovery ..."
c World Scientic Publishing Company THIRTY YEARS OF GRAPH MATCHING IN PATTERN RECOGNITION
, 2004
"... A recent paper posed the question: \Graph Matching: What are we really talking about?". Far from providing a denite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition eld. To this aim two taxonomies are presented and discu ..."
Abstract
 Add to MetaCart
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A recent paper posed the question: \Graph Matching: What are we really talking about?". Far from providing a denite answer to that question, in this paper we will try to characterize the role that graphs play within the Pattern Recognition eld. To this aim two taxonomies are presented and discussed. The rst includes almost all the graph matching algorithms proposed from the late seventies, and describes the dierent classes of algorithms. The second taxonomy considers the types of common applications of graphbased techniques in the Pattern Recognition and Machine Vision eld.