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589
RASCAL: Calculation of graph similarity using maximum common edge subgraphs
 The Computer Journal
, 2002
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Space Efficient Hash Tables With Worst Case Constant Access Time
 In STACS
, 2003
"... We generalize Cuckoo Hashing [23] to dary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + ffl) n memory cells, for any constant ffl ? 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(ln ffl ) probes ..."
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Cited by 47 (4 self)
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We generalize Cuckoo Hashing [23] to dary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + ffl) n memory cells, for any constant ffl ? 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(ln ffl ) probes and the expected amortized insertion time is constant. This is the first dictionary that has worst case constant access time and expected constant update time, works with (1 + ffl) n space, and supports satellite information. Experiments indicate that d = 4 choices suffice for ffl 0:03. We also describe variants of the data structure that allow the use of hash functions that can be evaluted in constant time.
Hardness of Approximation for VertexConnectivity NetworkDesign Problems
, 2002
"... In the survivable network design problem (SNDP), the goal is to find a minimumcost spanning subgraph satisfying certain connectivity requirements. We study the vertexconnectivity variant of SNDP in which the input specifies, for each pair of vertices, a required number of vertexdisjoint paths con ..."
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Cited by 44 (5 self)
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In the survivable network design problem (SNDP), the goal is to find a minimumcost spanning subgraph satisfying certain connectivity requirements. We study the vertexconnectivity variant of SNDP in which the input specifies, for each pair of vertices, a required number of vertexdisjoint paths connecting them.
Graph Kernels and Gaussian Processes for Relational Reinforcement Learning
 Machine Learning
, 2003
"... Relational reinforcement learning is a Qlearning technique for relational stateaction spaces. It aims to enable agents to learn how to act in an environment that has no natural representation as a tuple of constants. In this case, the learning algorithm used to approximate the mapping between stat ..."
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Cited by 40 (9 self)
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Relational reinforcement learning is a Qlearning technique for relational stateaction spaces. It aims to enable agents to learn how to act in an environment that has no natural representation as a tuple of constants. In this case, the learning algorithm used to approximate the mapping between stateaction pairs and their so called Q(uality)value has to be not only very reliable, but it also has to be able to handle the relational representation of stateaction pairs. In this paper we investigate...
Expressivity versus efficiency of graph kernels
 Proceedings of the First International Workshop on Mining Graphs, Trees and Sequences
, 2003
"... Abstract. Recently, kernel methods have become a popular tool for machine learning and data mining. As most ‘realworld ’ data is structured, research in kernel methods has begun investigating kernels for various kinds of structured data. One of the most widely used tools for modeling structured dat ..."
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Cited by 35 (0 self)
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Abstract. Recently, kernel methods have become a popular tool for machine learning and data mining. As most ‘realworld ’ data is structured, research in kernel methods has begun investigating kernels for various kinds of structured data. One of the most widely used tools for modeling structured data are graphs. In this paper we study the tradeoff between expressivity and efficiency of graph kernels. First, we motivate the need for this discussion by showing that fully general graph kernels can not even be approximated efficiently. We also discuss generalizations of graph kernels defined in literature and show that they are either not positive definite or not very useful. Finally, we propose a new graph kernel based on subtree patterns. We argue that while a little more computationally expensive, this kernel is more expressive than kernels based on walks. 1
PageRank for Product Image Search
 IN: WWW 2008. REFEREED TRACK: RICH MEDIA
, 2008
"... In this paper, we cast the imageranking problem into the task of identifying “authority” nodes on an inferred visual similarity graph and propose an algorithm to analyze the visual link structure that can be created among a group of images. Through an iterative procedure based on the PageRank compu ..."
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Cited by 34 (0 self)
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In this paper, we cast the imageranking problem into the task of identifying “authority” nodes on an inferred visual similarity graph and propose an algorithm to analyze the visual link structure that can be created among a group of images. Through an iterative procedure based on the PageRank computation, a numerical weight is assigned to each image; this measures its relative importance to the other images being considered. The incorporation of visual signals in this process differs from the majority of largescale commercialsearch engines in use today. Commercial searchengines often solely rely on the text clues of the pages in which images are embedded to rank images, and often entirely ignore the content of the images themselves as a ranking signal. To quantify the performance of our approach in a realworld system, we conducted a series of experiments based on the task of retrieving images for 2000 of the most popular products queries. Our experimental results show significant improvement, in terms of user satisfaction and relevancy, in comparison to the most recent Google Image Search results.
FixedPoint Logics on Planar Graphs
 IN PROCEEDINGS OF THE 13TH IEEE SYMPOSIUM ON LOGIC IN COMPUTER SCIENCE
, 1998
"... We study the expressive power of inflationary fixedpoint logic IFP and inflationary fixedpoint logic with counting IFP+C on planar graphs. We prove the following results: (1) IFP captures polynomial time on 3connected planar graphs, and IFP+C captures polynomial time on arbitrary planar graphs. ..."
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Cited by 34 (12 self)
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We study the expressive power of inflationary fixedpoint logic IFP and inflationary fixedpoint logic with counting IFP+C on planar graphs. We prove the following results: (1) IFP captures polynomial time on 3connected planar graphs, and IFP+C captures polynomial time on arbitrary planar graphs. (2) Planar graphs can be characterized up to isomorphism in a logic with finitely many variables and counting. This answers a question of Immerman [7]. (3) The class of planar graphs is definable in IFP. This answers a question of Dawar and Grädel [16].
Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... We introduce a novel optimization method based on semidefinite programming relaxations to the field of computer vision and apply it to the combinatorial problem of minimizing quadratic functionals in binary decision variables subject to linear constraints. ..."
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Cited by 30 (6 self)
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We introduce a novel optimization method based on semidefinite programming relaxations to the field of computer vision and apply it to the combinatorial problem of minimizing quadratic functionals in binary decision variables subject to linear constraints.