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List sampling for large graphs
Intelligent Data Analysis, 2018Real world graphs are massive in size and often prohibitively expensive to analyze. Of the possible solutions, sampling is extracting a representative subgraph from a large graph that faithfully represents the actual graph. The prior research has developed several sampling methods but the samples produced by these methods fail to match important ...
Muhammad Irfan Yousuf, Suhyun Kim 0001
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On Sampling of Bandlimited Graph Signals
2018The signal processing on graphs has been widely used in various fields, including machine learning, classification and network signal processing, in which the sampling of bandlimited graph signals plays an important role. In this paper, we discuss the sampling of bandlimited graph signals based on the theory of function spaces, which is consistent with
Mo Han +3 more
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Random sampling in residual graphs
Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, 2002Consider an n-vertex, m-edge, undirected graph with maximum flow value v. We give a new O(m+nv)-time maximum flow algorithm based on finding augmenting paths in random samples of the edges of residual graphs. After assigning certain special sampling probabilities to edges in O(m) time, our algorithm is very simple: repeatedly find an augmenting path in
David R. Karger, Matthew S. Levine
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Scalable Graph Sampling on GPUs with Compressed Graph
Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022Hongbo Yin +4 more
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Algorithms for the Sample Mean of Graphs
2009Measures of central tendency for graphs are important for protoype construction, frequent substructure mining, and multiple alignment of protein structures. This contribution proposes subgradient-based methods for determining a sample mean of graphs. We assess the performance of the proposed algorithms in a comparative empirical study.
Brijnesh J. Jain, Klaus Obermayer
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Sampling and Inference in a Population Graph
International Statistical Review / Revue Internationale de Statistique, 1980Summary Graph models can be used in sample surveys utilizing a known or observable relational structure defined for pairs of units. This review paper gives an overview of some of the statistical inference problems which have been considered in connection with sampling from a population graph.
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Half Sampling on Bipartite Graphs
Journal of Fourier Analysis and Applications, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Sampling and Merging for Graph Anonymization
2016We propose a method for network anonymization that consists on sampling a subset of vertices and merging its neighborhoods in the network. In such a way, by publishing the merged graph of the network together with the sampled vertices and their locally anonymized neighborhoods, we obtain a complete anonymized picture of the network.
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An adaptive graph sampling framework for graph analytics
Social Network Analysis and Mining, 2023openaire +1 more source
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey
IEEE/CAA Journal of Automatica Sinica, 2022Mingyu Yan, Lei Deng, Xiaochun Ye
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