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Uncertain Graph Sparsification
[article]
2017
arXiv
pre-print
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To overcome this problem, we introduce the first sparsification techniques aimed explicitly at uncertain graphs. ...
However, adaptation of deterministic sparsification methods fails in the uncertain setting. ...
In order to tackle the high cost, we develop techniques for uncertain graph sparsification. ...
arXiv:1611.04308v4
fatcat:g534ej7hhrhn3f3hg2arzazzg4
Sparsification of long range force networks for molecular dynamics simulations
2019
PLoS ONE
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Spectral sparsification of the Lennard-Jones potential yields comparable results to thresholding while spectral sparsification yields improvements when considering a long-range Coulomb potential. ...
In particular, we quantify the performance of the spectral sparsification algorithm for the short-range Lennard-Jones potential and the long-range Coulomb potential. ...
However, the effects due to thresholding become uncertain when using long-range forces such as those produced from electrostatic and magnetostatic problems. ...
doi:10.1371/journal.pone.0213262
pmid:30978200
pmcid:PMC6461233
fatcat:6tgbtgujxrea7cpafpwdi2n3ne
A Survey on Mining and Analysis of Uncertain Graphs
[article]
2021
arXiv
pre-print
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Uncertain Graph (also known as Probabilistic Graph) is a generic model to represent many realworld networks from social to biological. ...
In recent times analysis and mining of uncertain graphs have drawn significant attention from the researchers of the data management community. ...
-Uncertain Graph Sparsification: A Plethora of literature is available for sparsification of a deterministic graph [8, 39, 94] . ...
arXiv:2106.07837v1
fatcat:f3wuv5rhqnhk5lbhdmzl7f2bgm
Injecting uncertainty in graphs for identity obfuscation
2012
Proceedings of the VLDB Endowment
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In this paper we introduce a new anonymization approach that is based on injecting uncertainty in social graphs and publishing the resulting uncertain graphs. ...
Unfortunately, publishing socialnetwork graphs is considered an ill-advised practice due to privacy concerns. ...
compute them efficiently in uncertain graphs. ...
doi:10.14778/2350229.2350254
fatcat:gsgjxjmgzfg77cwk4ak55cbxfe
Injecting Uncertainty in Graphs for Identity Obfuscation
[article]
2012
arXiv
pre-print
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In this paper we introduce a new anonymization approach that is based on injecting uncertainty in social graphs and publishing the resulting uncertain graphs. ...
Unfortunately, publishing social-network graphs is considered an ill-advised practice due to privacy concerns. ...
compute them efficiently in uncertain graphs. ...
arXiv:1208.4145v1
fatcat:vqre6fxqpve5dj2nm4k6my2hxy
A unified resource-constrained framework for graph SLAM
2016
2016 IEEE International Conference on Robotics and Automation (ICRA)
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We formulate the node selection problem as a minimization problem over the penalty to be paid in the resulting sparsification. ...
Incremental solvers are able to process incoming sensor data and produce maximum a posteriori (MAP) estimates in realtime by exploiting the natural sparsity within the graph for reasonable-sized problems ...
Sparsification of edges While marginalization reduces the size of the graph, it adversely increases the density of the graph. ...
doi:10.1109/icra.2016.7487268
dblp:conf/icra/PaullHL16
fatcat:tq7hkabdnza6vl6cac3rvnxplm
Identity obfuscation in graphs through the information theoretic lens
2014
Information Sciences
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We thus study the resilience of obfuscation by random sparsification to adversarial attacks that are based on link prediction. ...
In addition, we introduce and explore the method of random sparsification, which randomly removes edges, without adding new ones. ...
Their approach is based on injecting uncertainty to the edges of the social graph and publishing the resulting uncertain graph. ...
doi:10.1016/j.ins.2014.02.035
fatcat:2fncvx5jr5hitlpr64cs4usaeq
Identity obfuscation in graphs through the information theoretic lens
2011
2011 IEEE 27th International Conference on Data Engineering
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We thus study the resilience of obfuscation by random sparsification to adversarial attacks that are based on link prediction. ...
In addition, we introduce and explore the method of random sparsification, which randomly removes edges, without adding new ones. ...
Their approach is based on injecting uncertainty to the edges of the social graph and publishing the resulting uncertain graph. ...
doi:10.1109/icde.2011.5767905
dblp:conf/icde/BonchiGT11
fatcat:fttny654kzfe5jyvpmkqdvxm3q
Feedback Controller Sparsification for a Class of Linear Systems with Parametric Uncertainties
[article]
2018
arXiv
pre-print
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We consider the problem of output feedback controller sparsification for systems with parametric uncertainties. ...
We develop an optimization scheme that minimizes the performance deterioration caused by the sparsification process, while enhancing sparsity pattern of the feedback gain. ...
Similar plots for the case of uncertain system with ρ rel = 30% are depicted in Figure 6b . ...
arXiv:1805.05556v1
fatcat:r5ghnc7tsjdwbold63ovma66n4
Uncertainty Quantification of Trajectory Clustering Applied to Ocean Ensemble Forecasts
[article]
2020
arXiv
pre-print
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This fact highlights the coherent structure robustness, even in uncertain conditions. ...
However, for a fixed percent sparsification, the graph connections that are retained are ultimately a function of the number of particles and their distribution in the initial grid. ...
arXiv:2008.12253v1
fatcat:dlrbsb4vqnhprj7lw4ltqi2vqy
Adaptive autonomous control using online value iteration with gaussian processes
2009
2009 IEEE International Conference on Robotics and Automation
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Additionally, to reduce computation time and to make the system applicable to online learning, we present an efficient sparsification method. ...
To solve the second integral, the prediction of the value at state s ′ is approximated by GP v where the input itself is uncertain. ...
Furthermore, the corresponding error to a base-line policy is shown in the lower graph. ...
doi:10.1109/robot.2009.5152660
dblp:conf/icra/RottmannB09
fatcat:fcaxfbminfaarinom7bhusoooa
Control Strategies of Human Interactive Robot Under Uncertain Environments
[chapter]
2011
Mobile Robots - Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training
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Actually, there are kinds of solving approaches for uncertainties according to circumstances: some uncertain can be assumed as Gaussian noise and based on the property of Gaussian noise, we can do estimation ...
After computing the sparse ratios of the information matrices in Fig. 2 , we can get the graph that illustrates how the sparse ratio changes with the threshold (Fig. 3) where the x-axis denotes the ...
Control Strategies of Human Interactive Robot Under Uncertain Environments, Mobile Robots -Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training, Dr. ...
doi:10.5772/25508
fatcat:mu3ab3k6dfdhjofdtyvtbupyhy
Scale-resolved analysis of brain functional connectivity networks with spectral entropy
[article]
2019
bioRxiv
pre-print
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Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges ...
Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information ...
A most contentious methodological issue in graph analysis, as applied to the study of brain connectivity, is the one of network sparsification. ...
doi:10.1101/813162
fatcat:yv4wbedcuzbp3ih2ac3cqhn3ze
GRAND+: Scalable Graph Random Neural Networks
[article]
2022
arXiv
pre-print
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Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on graphs. ...
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning. ...
Top-𝑘 Sparsification. To further reduce training cost, we perform top-𝑘 sparsification for Π 𝑠 . ...
arXiv:2203.06389v1
fatcat:vt27na7majg3bo6hatxzygiwvm
Towards Unsupervised Deep Graph Structure Learning
[article]
2022
arXiv
pre-print
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In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. ...
Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph. ...
Spectral uncertain networks. Physical Review E 93, 1 (2016), 012306.
networks and locally connected networks on graphs. ...
arXiv:2201.06367v1
fatcat:ew3msx6p6vc5hadgkryoixhyuq
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