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Large-scale network motif analysis using compression
[article]
2019
arXiv
pre-print
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This allows motif analysis to scale to networks with billions of links. ...
We introduce a new method for finding network motifs: interesting or informative subgraph patterns in a network. ...
Introduction Graphlets are small, induced subgraphs in a large network. Network motifs [35] are those graphlets that occur more frequently in the data than expected. ...
arXiv:1701.02026v3
fatcat:mgrse5y4ojg5bftghc7z6vsube
Large-scale network motif analysis using compression
2020
Data mining and knowledge discovery
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This allows motif analysis to scale to networks with billions of links. ...
We introduce a new method for finding network motifs. Subgraphs are motifs when their frequency in the data is high compared to the expected frequency under a null model. ...
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long ...
doi:10.1007/s10618-020-00691-y
fatcat:ze2fwwj7r5epliorxnjqju6vve
RMOD: A Tool for Regulatory Motif Detection in Signaling Network
2013
PLoS ONE
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To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query ...
RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. ...
matched with the compressed forms of regulatory motifs on large-scale signaling networks. ...
doi:10.1371/journal.pone.0068407
pmid:23874612
pmcid:PMC3710000
fatcat:y7quxeliojbrxl45h5rhzteiwm
Quantifying loss of information in network-based dimensionality reduction techniques
2015
Journal of Complex Networks
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However, the approach shows that network motif analysis excels at preserving the relative algorithmic information content of a network, hence validating and generalizing the remarkable fact that despite ...
Here, we develop a framework, based on algorithmic information theory, to quantify the extent to which information is preserved when network motif analysis, graph spectra and spectral sparsification methods ...
This means that when using BDM, graph motif and compressibility analysis, order is better preserved among networks of the same family than among different families. ...
doi:10.1093/comnet/cnv025
fatcat:loarnheyljgvxdaxww5lvksfpe
Quantifying Loss of Information in Network-based Dimensionality Reduction Techniques
[article]
2015
arXiv
pre-print
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However, the approach shows that network motif analysis excels at preserving the relative algorithmic information content of a network, hence validating and generalizing the remarkable fact that despite ...
Here we develop a framework, based on algorithmic information theory, to quantify the extent to which information is preserved when network motif analysis, graph spectra and spectral sparsification methods ...
on the global scale through applying lossless compressibility to the complete networks. ...
arXiv:1504.06249v4
fatcat:7jijnayfnreejjexjogdjnz42u
Complex Network Analysis Using Parallel Approximate Motif Counting
2014
2014 IEEE 28th International Parallel and Distributed Processing Symposium
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Subgraph counting forms the basis of many complex network analysis metrics, including motif and anti-motif finding, relative graphlet frequency distance, and graphlet degree distribution agreements. ...
The new additions let us use subgraph counts as graph signatures for a large network collection, and we analyze this collection using various subgraph count-based graph analytics. ...
ACKNOWLEDGMENT This work is supported by NSF grant ACI-1253881 and used instrumentation funded by the NSF grant OCI-0821527. ...
doi:10.1109/ipdps.2014.50
dblp:conf/ipps/SlotaM14
fatcat:uynrdx33p5dtxktuzaypdizphq
Rigid Graph Compression: Motif-Based Rigidity Analysis for Disordered Fiber Networks
2018
Multiscale Modeling & simulation
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Using particle-scale models to accurately describe property enhancements and phase transitions in macroscopic behavior is a major engineering challenge in composite materials science. ...
We develop an efficient algorithmic approach called rigid graph compression (RGC) to describe the transition from floppy to rigid in disordered fiber networks ("rod-hinge systems"), which form the reinforcing ...
In Sec. 3.2, we use this foundation to identify primitive rigid motifs that will serve as the building blocks of large rigid networks. ...
doi:10.1137/17m1157271
pmid:30450018
pmcid:PMC6234004
fatcat:uvwq5wzgnbhh7ajgx6mrlbsv3u
Efficient community detection using power graph analysis
2011
Proceedings of the 9th workshop on Large-scale and distributed informational retrieval - LSDS-IR '11
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The large scale, but also the complexity, of these types of networks constitutes the need for efficient graph mining algorithms. ...
art methods for the same task, especially in networks with large numbers of nodes. ...
The benefits of applying Power Graph Analysis for community detection are twofold: (1) the methodology allows for fast and large-scale node clustering experiments, since it can compress by even up to 90% ...
doi:10.1145/2064730.2064738
fatcat:2l3gba24urhk7muaccmjbcdltu
Network Compression as a Quality Measure for Protein Interaction Networks
2012
PLoS ONE
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Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. ...
With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? ...
for large-scale protein interaction mapping. ...
doi:10.1371/journal.pone.0035729
pmid:22719828
pmcid:PMC3377704
fatcat:g4ozprvtrnc3zjzyzewe7y5io4
Networks of monetary flow at native resolution
[article]
2019
arXiv
pre-print
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We aim to understand how such activity gives rise to large-scale patterns of monetary flow. ...
Our methodology relates economic activity at the transaction level to large-scale patterns of monetary flow, broadening the scope of empirical study about the network and temporal structure of the economy ...
The time scale at which walkers are moving is the time scale at which the network itself changes, and this seriously complicates analysis. ...
arXiv:1910.05596v1
fatcat:a5cv5sqvnrex7fedvilkuk6pmi
Motifs in bipartite ecological networks: uncovering indirect interactions
2018
Oikos
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However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. ...
Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. ...
BIS, ARC and NJB performed the analysis demonstrating meso-scale dissimilarity in networks with similar macro-scale properties. HSW assisted with analyses. ...
doi:10.1111/oik.05670
fatcat:gnbwngvc7rgihla5hcsij3syv4
KCoreMotif: An Efficient Graph Clustering Algorithm for Large Networks by Exploiting k-core Decomposition and Motifs
[article]
2020
arXiv
pre-print
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Clustering analysis has been widely used in trust evaluation on various complex networks such as wireless sensors networks and online social networks. ...
To deal with large networks, in this paper, we propose an efficient graph clustering algorithm, KCoreMotif, specifically for large networks by exploiting k-core decomposition and motifs. ...
Nyström extension method was originally used to solve large-scale matrix eigendecomposition problems. ...
arXiv:2008.10380v1
fatcat:xvd5g3ciknbdfgpkweximeajs4
Uncovering indirect interactions in bipartite ecological networks
[article]
2018
bioRxiv
pre-print
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However, compressing a complex network into a single metric necessarily discards large amounts of information about indirect interactions. ...
Here we use the emerging concept of bipartite motifs to outline a new framework for bipartite networks that incorporates indirect interactions. ...
ARC and NJB performed the analysis demonstrating meso-scale dissimilarity in networks with similar macro-scale properties. All authors contributed to writing the manuscript. ...
doi:10.1101/315010
fatcat:w6qqrixc6fd2hcgickg73mvciu
Spectral peculiarity and criticality of the human connectome
[article]
2018
arXiv
pre-print
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We have performed the comparative spectral analysis of structural connectomes for various organisms using open-access data. ...
Our analysis indicates several new peculiar features of the human connectome. ...
Thus in a huge number of works the brain networks are studied at large and middle scales. ...
arXiv:1812.06317v1
fatcat:7resmeea75ewjc7ms4yjjnelci
A Computational model for compressed sensing RNAi cellular screening
2012
BMC Bioinformatics
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Conclusions: This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and ...
To build this model, we first search nucleotide motifs in a target gene set. ...
We drew this figure of network using Cytoscape, an open source platform for complex-network analysis and visualization [37] . ...
doi:10.1186/1471-2105-13-337
pmid:23270311
pmcid:PMC3544734
fatcat:z7iowsd2vjf2nosbqlv77655ny
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