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Results 1 - 10
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298
Approximation Algorithms for Connected Dominating Sets
- Algorithmica
, 1996
"... The dominating set problem in graphs asks for a minimum size subset of vertices with the following property: each vertex is required to either be in the dominating set, or adjacent to some node in the dominating set. We focus on the question of finding a connected dominating set of minimum size, whe ..."
Abstract
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Cited by 366 (9 self)
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degree, and H is the harmonic function. This question also arises in relation to the traveling tourist problem, where one is looking for the shortest tour such that each vertex is either visited, or has at least one of its neighbors visited. We study a generalization of the problem when the vertices have
Empirical comparison of algorithms for network community detection
- In Proc. WWW’10
, 2010
"... Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity ..."
Abstract
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Cited by 171 (5 self)
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connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that “look like” good communities for the application of interest. In this paper, we explore a range of network community detection
Graph mining: laws, generators, and algorithms
- ACM COMPUT SURV (CSUR
, 2006
"... How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M: N relation in ..."
Abstract
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Cited by 132 (7 self)
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How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M: N relation
A convolutional neural network for modelling sentences.
- In Proceedings of the 52th Annual Meeting of the Association for Computational Linguistics.
, 2014
"... Abstract The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global poolin ..."
Abstract
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Cited by 59 (2 self)
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pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test
A Closer Look at Structural Similarity in Analogical Transfer
, 2000
"... We propose to characterize structural similarity between source and target problems by the type and size of their structural overlap. Size of structural overlap is captured by a measure of graph-distance. We investigated the influence of structural overlap on transfer success in analogical problem s ..."
Abstract
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Cited by 5 (3 self)
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We propose to characterize structural similarity between source and target problems by the type and size of their structural overlap. Size of structural overlap is captured by a measure of graph-distance. We investigated the influence of structural overlap on transfer success in analogical problem
Diffusion-Convolutional Neural Networks
"... Abstract We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graphstructured data and used as an effective basis for node cla ..."
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Abstract We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graphstructured data and used as an effective basis for node
Relation Classification via Convolutional Deep Neural Network
"... The state-of-the-art methods used for relation classification are primarily based on statistical ma-chine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language process-ing ( ..."
Abstract
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Cited by 11 (1 self)
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(NLP) systems, which leads to the propagation of the errors in the existing tools and hinders the performance of these systems. In this paper, we exploit a convolutional deep neural network (DNN) to extract lexical and sentence level features. Our method takes all of the word tokens as input without
Whom You Know Matters: Venture Capital Networks and Investment Performance,
- Journal of Finance
, 2007
"... Abstract Many financial markets are characterized by strong relationships and networks, rather than arm's-length, spot-market transactions. We examine the performance consequences of this organizational choice in the context of relationships established when VCs syndicate portfolio company inv ..."
Abstract
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Cited by 138 (8 self)
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graph theory, a mathematical discipline widely used in economic sociology. 2 Graph theory provides us with tools for describing networks at a "macro" level and for measuring the relative importance, or "centrality," of each actor in the network. Our centrality measures capture five
Degree relations of triangles in realworld networks and graph models
- in CIKM ’12: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM ’12
"... Triangles are an important building block and distinguishing feature of real-world networks, but their structure is still poorly understood. Despite numerous reports on the abundance of triangles, there is very little information on what these triangles look like. We initiate the study of degreelabe ..."
Abstract
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Cited by 6 (4 self)
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of degreelabeled triangles — specifically, degree homogeneity versus heterogeneity in triangles. This yields new insight into the structure of real-world graphs. We observe that networks coming from social and collaborative situations are dominated by homogeneous triangles, i.e., degrees of vertices in a triangle
Comparing Convolution Kernels and Recursive Neural Networks for Learning Preferences on Structured Data
"... Convolution kernels and recursive neural networks (RNN) are both suitable approaches for supervised learning when the input portion of an instance is a discrete structure like a tree or a graph. We report about an empirical comparison between the two architectures in a large scale preference learnin ..."
Abstract
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Cited by 1 (1 self)
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Convolution kernels and recursive neural networks (RNN) are both suitable approaches for supervised learning when the input portion of an instance is a discrete structure like a tree or a graph. We report about an empirical comparison between the two architectures in a large scale preference
Results 1 - 10
of
298
