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Uncertain Graph Sparsification [article]

Panos Parchas, Nikolaos Papailiou, Dimitris Papadias, Francesco Bonchi
2017 arXiv   pre-print
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

Peter Woerner, Aditya G. Nair, Kunihiko Taira, William S. Oates, Elena Papaleo
2019 PLoS ONE  
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]

Suman Banerjee
2021 arXiv   pre-print
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

Paolo Boldi, Francesco Bonchi, Aristides Gionis, Tamir Tassa
2012 Proceedings of the VLDB Endowment  
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]

Paolo Boldi, Francesco Bonchi, Aris Gionis, Tamir Tassa
2012 arXiv   pre-print
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

Liam Paull, Guoquan Huang, John J. Leonard
2016 2016 IEEE International Conference on Robotics and Automation (ICRA)  
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

Francesco Bonchi, Aristides Gionis, Tamir Tassa
2014 Information Sciences  
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

Francesco Bonchi, Aristides Gionis, Tamir Tassa
2011 2011 IEEE 27th International Conference on Data Engineering  
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]

Reza Arastoo, MirSaleh Bahavarnia
2018 arXiv   pre-print
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]

Guilherme S. Vieira, Irina I. Rypina, Michael R. Allshouse
2020 arXiv   pre-print
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

A. Rottmann, W. Burgard
2009 2009 IEEE International Conference on Robotics and Automation  
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]

Haiwei Dong, Zhiwei Luo
2011 Mobile Robots - Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training  
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]

Carlo Nicolini, Giulia Forcellini, Ludovico Minati, Angelo Bifone
2019 bioRxiv   pre-print
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]

Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang
2022 arXiv   pre-print
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]

Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan
2022 arXiv   pre-print
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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