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826
Local treewidth, excluded minors, and approximation algorithms
 Combinatorica
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RASCAL: Calculation of Graph Similarity using Maximum Common Edge Subgraphs
, 2002
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A Graph Theoretic Approach to Software Watermarking
, 2001
"... We present a graph theoretic approach for watermarking software in a robust fashion. While watermarking software that are small in size (e.g. a few kilobytes) may be infeasible through this approach, it seems to be a viable scheme for large applications. Our approach works with control/data flow gra ..."
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Cited by 53 (0 self)
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We present a graph theoretic approach for watermarking software in a robust fashion. While watermarking software that are small in size (e.g. a few kilobytes) may be infeasible through this approach, it seems to be a viable scheme for large applications. Our approach works with control/data flow graphs and uses abstractions,approximate kpartitions,and a random walk method to embed the watermark,with the goal of minimizing and controlling the additions to be made for embedding,while keeping the estimated effort to undo the watermark (WM) as high as possible. The watermarks are so embedded that small changes to the software or flow graph are unlikely to disable detection by a probabilistic algorithm that has a secret. This is done by using some relatively robust graph properties and error correcting codes. Under some natural assumptions about the code added to embed the WM,locating the WM by an attacker is related to some graph approximation problems. Since little theoretical foundation exists for hardness of typical instances of graph approximation problems,we present heuristics to generate such hard instances and,in a limited case,present a heuristic analysis of how hard it is to separate the WM in an information theoretic model. We describe some related experimental work. The approach and methods described here also suitable for solving the problem of software tamper resistance.
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.
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 43 (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...
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 41 (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.
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 40 (4 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.
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 39 (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
Fixedpoint logics on planar graphs
, 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. (2 ..."
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Cited by 35 (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].