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On robust network coding subgraph construction under uncertainty
2008
2008 42nd Asilomar Conference on Signals, Systems and Computers
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We consider the problem of network coding subgraph construction in networks where there is uncertainty about link loss rates. ...
For a given set of scenarios specified by an uncertainty set of link loss rates, we provide a robust optimizationbased formulation to construct a single subgraph that would work relatively well across ...
In this paper, we consider the problem of network coding subgraph construction that is robust against uncertainty about link loss rates. ...
doi:10.1109/acssc.2008.5074828
fatcat:ooxrbu7jpnfnjdm77gkheoico4
Robust Densest Subgraph Discovery
[article]
2018
arXiv
pre-print
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In this study, we provide a framework for dense subgraph discovery under the uncertainty of edge weights. Specifically, we address such an uncertainty issue using the theory of robust optimization. ...
Although the densest subgraph problem is one of the most well-studied optimization problems for dense subgraph discovery, there is an implicit strong assumption; it is assumed that the weights of all the ...
To the best of our knowledge, we are the first to utilize the theory of robust optimization for addressing dense subgraph discovery under uncertainty. ...
arXiv:1809.04802v1
fatcat:4zawd5qtpndnxgqtiv3f23apfi
Robust Capacity Planning in Network Coding under Demand Uncertainty
2015
KSII Transactions on Internet and Information Systems
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In this paper, we address the problem of capacity provisioning in a network subject to demand uncertainty such that a network coded multicast is applied as the data delivery mechanism with limited budget ...
A major challenge in network service providers is to provide adequate resources in service level agreements based on forecasts of future demands. ...
[3] proposed a distributed linear code construction for the deterministic wireless multicast relay network model. ...
doi:10.3837/tiis.2015.08.005
fatcat:3cjoyiwyonfqngbaozkryp4tda
Network coding-based multicast in multi-hop CRNs under uncertain spectrum availability
2015
2015 IEEE Conference on Computer Communications (INFOCOM)
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The benefits of network coding on multicast in traditional multi-hop wireless networks have already been demonstrated in previous works. ...
Extensive simulation results show that, the proposed algorithm achieves higher multicast rates, compared to a stateof-the-art non-network coding algorithm in multi-hop CRNs, and a conservative robust algorithm ...
Indeed, most existing works on network coding-based multicast cannot handle new challenges arising from CRNs, including the uncertainty of spectrum availability. ...
doi:10.1109/infocom.2015.7218448
dblp:conf/infocom/QuDDWTWT15
fatcat:2puxy7qhmvhnzdm72cniesteca
Evolution leads to emergence: An analysis of protein interactomes across the tree of life
[article]
2020
bioRxiv
pre-print
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One consequence of this black-box nature is that it is unclear at which scale to analyze biological systems to best understand their function. ...
Due to the prevalence of noise and degeneracy in evolved systems, in many cases the workings of everything from gene regulatory networks to protein-protein interactome networks remain black boxes. ...
use of the effective information (EI), an information-theoretic network quantity based on the 76 entropy of random walker behavior on a network. ...
doi:10.1101/2020.05.03.074419
fatcat:ictyqlhcznbptlrvf6pve3fdt4
Functional brain network reconfiguration during learning in a dynamic environment
[article]
2019
bioRxiv
pre-print
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Here we identified a pattern of whole-brain functional connectivity that changed dynamically over time, being more strongly expressed under conditions of high surprise or uncertainty, and being enhanced ...
Constructing time-varying functional networks In order to construct dynamic functional networks, we define sliding time windows and calculate Pearson correlation coefficients between ROI time series in ...
No other subgraph showed such robust effects for both CPP and RU. ...
doi:10.1101/800284
fatcat:vk2veiubejhhzenagtmqeirynm
From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology
[article]
2018
arXiv
pre-print
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on incident reports from Imperial College Healthcare NHS Trust, London. ...
Yet this source of detailed information often remains under-used because of a lack of methodologies to extract interpretable content in a timely manner. ...
We find that our methodology provides improved performance over LDA models, as quantified by the Uncertainty Coefficient against the hand-coded categories. ...
arXiv:1807.02599v1
fatcat:y2gf4bpsy5abngyidt2gecc7ry
Self-stabilizing and Byzantine-Tolerant Overlay Network
[chapter]
2007
Lecture Notes in Computer Science
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Network overlays have been the subject of intensive research in recent years. ...
The overlay structure has a logarithmic diameter with high probability, which matches the diameter of less robust overlays. ...
as their predecessors on the different subgraphs. ...
doi:10.1007/978-3-540-77096-1_25
fatcat:pctcvn3zq5c6dj5aqmypn3y3h4
Node similarity within subgraphs of protein interaction networks
2008
Physica A: Statistical Mechanics and its Applications
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2007 accepted arXiv:0707.2076v2 fatcat:6admz3525vdt7csd6lnyqyb4d4Other Versions
The graph animal algorithm is used to estimate twinness for each pair of nodes (for subgraph sizes n=4 to n=12) in four different protein interaction networks (PINs). ...
We propose a biologically motivated quantity, twinness, to evaluate local similarity between nodes in a network. ...
Hence, in cases where the results are not demonstrably robust over current data sets, one can expect conclusions based on these results to change as the methods to construct PINs improve. ...
doi:10.1016/j.physa.2008.02.043
fatcat:oazslcuxh5dv3owl6uwqkwmk5a
An Efficient and Robust Star Identification Algorithm Based on Neural Networks
2021
Sensors
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A lost-in-space star identification algorithm based on a one-dimensional Convolutional Neural Network (1D CNN) is proposed. ...
With the help of neural networks, the robustness and the speed of the star identification are improved greatly. ...
Future work will focus on improving robustness under high dynamic conditions and the hardware performance. Figure 1 . 1 Figure 1. Structure of the Network.
Figure 2 . 2 Figure 2. ...
doi:10.3390/s21227686
pmid:34833762
pmcid:PMC8620066
fatcat:cnybw25ykvaqvazkvbpkonely4
Graph Scan Statistics With Uncertainty
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Scan statistics is one of the most popular approaches for anomaly detection in spatial and network data. In practice, there are numerous sources of uncertainty in the observed data. ...
We study two formulations for robust scan statistics, one based on the sample average approximation and the other using a max-min objective. ...
The dataset includes "ground truth" subgraphs representing parts of the network that are contaminated at different points in time-there is one ground truth graph for each snapshot. ...
doi:10.1609/aaai.v32i1.11799
fatcat:u243dvto3rcu7fxfve5e5cbslq
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach
[article]
2018
Applied Network Science
pre-print
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To do so, we combine recently developed deep neural network text-embedding methodologies based on paragraph vectors with multi-scale Markov Stability community detection applied to a similarity graph of ...
at different levels of resolution based directly on the free text descriptions contained within them. ...
Comparison of Markov Stability applied to Doc2Vec versus BoW (using TF-iDF) similarity graphs obtained under the same graph constructions steps. a Similarity against the externally hand-coded categories ...
doi:10.1007/s41109-018-0109-9
pmid:30906850
pmcid:PMC6400329
arXiv:1811.05711v1
fatcat:jcg67d32mvdkpkpuu4tfkhrzdq
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
[article]
2024
arXiv
pre-print
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Subsequently, we provide detailed discussions on these four aspects, dissecting how these solutions contribute to enhancing the reliability and robustness of GNN models. ...
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and network security. ...
the mutual information of the constructed multi-view subgraphs. ...
arXiv:2403.04468v1
fatcat:l3c2ebibxzde7mztvbbfpazuwa
Data-driven prediction and origin identification of epidemics in population networks
[article]
2020
arXiv
pre-print
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Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. ...
We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational ...
In contrast, placing sensors on opposite sides of the network to gain information about dynamics on different time scales yielded significantly less uncertainty. ...
arXiv:1710.07880v2
fatcat:mbunmfuqrzay7lif6vfs32ehba
Functional brain network reconfiguration during learning in a dynamic environment
2020
Nature Communications
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Here we show that modulations of surprise and uncertainty are encoded in a particular, temporally dynamic pattern of whole-brain functional connectivity, and this encoding is enhanced in individuals that ...
Code availability Code is available at https://github.com/changhaokao/nmf_network_learning. ...
Constructing time-varying functional networks. ...
doi:10.1038/s41467-020-15442-2
pmid:32245973
fatcat:gbgoy3xgcbe4xb365n7l3w4ja4
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