Results 31 to 40 of about 184,942 (303)
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014 Sampling is a standard approach in big-graph analytics; the goal is to efficiently estimate the graph properties by consulting a sample of the whole population. A perfect sample is assumed to mirror every property of the whole population. Unfortunately, such a perfect sample is hard to collect in complex populations such as graphs (e.g.
Nesreen K. Ahmed +3 more
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SORAG: Synthetic Data Over-Sampling Strategy on Multi-Label Graphs
Remote Sensing, 2022 In many real-world networks of interest in the field of remote sensing (e.g., public transport networks), nodes are associated with multiple labels, and node classes are imbalanced; that is, some classes have significantly fewer samples than others ...
Yijun Duan +6 more
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Evaluation of respondent-driven sampling [PDF]
, 2012 Respondent-driven sampling produced a generally representative sample of this well-connected nonhidden population. However, current respondent-driven sampling inference methods failed to reduce bias when it occurred.
Joseph Katongole +34 more
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Mosar: Efficiently Characterizing Both Frequent and Rare Motifs in Large Graphs
Applied Sciences, 2022 Due to high computational costs, exploring motif statistics (such as motif frequencies) of a large graph can be challenging. This is useful for understanding complex networks such as social and biological networks. To address this challenge, many methods
Wenhua Guo +4 more
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Tractable diffusion and coalescent processes for weakly correlated loci [PDF]
, 2015 Widely used models in genetics include the Wright-Fisher diffusion and its moment dual, Kingman's coalescent. Each has a multilocus extension but under neither extension is the sampling distribution available in closed-form, and their computation is ...
Fearnhead, Paul +6 more
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A second look at counting triangles in graph streams [PDF]
, 2014 In this paper we present improved results on the problem of counting triangles in edge streamed graphs. For graphs with m edges and at least T triangles, we show that an extra look over the stream yields a two-pass streaming algorithm that uses O((m)/(ε4.
Jowhari, Hossein, Cormode, Graham
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On Sampling Colorings of Bipartite Graphs [PDF]
Discrete Mathematics & Theoretical Computer Science, 2006 We study the problem of efficiently sampling k-colorings of bipartite graphs. We show that a class of markov chains cannot be used as efficient samplers. Precisely, we show that, for any k, 6 ≤ k ≤ n^\1/3-ε \, ε > 0 fixed, \emphalmost every bipartite graph on n+n vertices is such that the mixing time of any markov chain asymptotically uniform on its
R. Balasubramanian, C. R. Subramanian
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Fourier Analysis of Stochastic Sampling Strategies for Assessing Bias and Variance in Integration [PDF]
, 2013 Each pixel in a photorealistic, computer generated picture is calculated by approximately integrating all the light arriving at the pixel, from the virtual scene. A common strategy to calculate these high-dimensional integrals is to average the estimates
Kautz, Jan, Subr, Katric
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Negative Sampling Method for Fusing Knowledge Graph [PDF]
Jisuanji kexueIn order to solve the problem of information overload,recommender systems have been widely studied.Since it is difficult to obtain a large amount of high-quality explicit feedback data,implicit feedback data becomes the mainstream choice for training re ...
LU Haiyang, LIU Xianhui, HOU Wenlong
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Accelerating graph sampling for graph machine learning using GPUs [PDF]
Proceedings of the Sixteenth European Conference on Computer Systems, 2021 Published in EuroSys ...
Abhinav Jangda +3 more
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