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Change Point Methods on a Sequence of Graphs [article]

Daniele Zambon, Cesare Alippi, Lorenzo Livi
2018 arXiv   pre-print
Novel Change Point Methods (CPMs) are proposed, that (i) map graphs into a vector domain; (ii) apply a suitable statistical test in the vector space; (iii) detect the change --if any-- according to a confidence  ...  Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process  ...  project 200021_172671: "ALPSFORT: A Learning graPh-baSed framework FOr cybeR-physical sysTems".  ... 
arXiv:1805.07113v2 fatcat:ktj2ftkvvrdlphugmxqj77pvyq

Graph similarity learning for change-point detection in dynamic networks [article]

Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong
2022 arXiv   pre-print
We show on synthetic and real data that our method enjoys a number of benefits: it is able to learn an adequate graph similarity function for performing online network change-point detection in diverse  ...  Importantly, our method does not require prior knowledge on the network generative distribution and is agnostic to the type of change-points; moreover, it can be applied to a large variety of networks,  ...  The second task consists in localising a single change-point in a dynamic SBM sequence. We remark that for very large values of p, many methods attain zero error.  ... 
arXiv:2203.15470v1 fatcat:dax7fxusazetva2a6wb4z35hky

A Universal Nonparametric Event Detection Framework for Neuropixels Data [article]

Hao Chen, Shizhe Chen, Xinyi Deng
2019 bioRxiv   pre-print
changes as well as changes that only occur in a subset of the neural population, referred to as "change-points".  ...  The sequence of change-point events can be interpreted as a footprint of neural population activities, which allows us to relate behavior to simultaneously recorded high-dimensional neural activities across  ...  XD additionally thanks Liam Paninski for organizational support during her time on this project.  ... 
doi:10.1101/650671 fatcat:yhlr42lrjzhvjfpxep52zwj4oy

Network topology change-point detection from graph signals with prior spectral signatures [article]

Chiraag Kaushik, T. Mitchell Roddenberry, Santiago Segarra
2020 arXiv   pre-print
We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection  ...  We consider the problem of sequential graph topology change-point detection from graph signals.  ...  NETWORK CHANGE-POINT DETECTION Consider a streaming sequence of graphs with a single change point.  ... 
arXiv:2010.11345v1 fatcat:53mpt5rrevc5pkihlwldrp7hxq

Online Graph-Based Change Point Detection in Multiband Image Sequences [article]

Ricardo Augusto Borsoi, Cédric Richard, André Ferrari, Jie Chen, José Carlos Moreira Bermudez
2020 arXiv   pre-print
Acting on neighboring spectra as adjacent vertices in a graph, this algorithm focuses on anomalies concurrently activating groups of vertices corresponding to compact, well-connected and spectrally homogeneous  ...  Experiments illustrate the detection and localization performance of the method.  ...  Determining whether t is a change point of Y t is now equivalent to testing if a change occurred on at least one of the vertices, n = 1, . . . , N , which consists of a multiple testing problem.  ... 
arXiv:2006.14033v1 fatcat:7cbavvjuhfdzjauqdj3putb2oq

Semi-Automatic Time-Series Transfer Functions via Temporal Clustering and Sequencing

Jonathan Woodring, Han-Wei Shen
2009 Computer graphics forum (Print)  
Then, sequencing derives a progression of clusters over time, creating chains that follow value distribution changes.  ...  We utilize a method we call temporal clustering and sequencing to find dynamic features in value space and create a corresponding transfer function.  ...  The method attempts to generate a classification for a time series data set by identifying groups of points that change in value similarly [FMHC07, vWvS99, WS09] and creating sequences of groups over  ... 
doi:10.1111/j.1467-8659.2009.01472.x fatcat:bteo2ed7erhcrjq322ntrnftve

A Fast and Efficient Change-point Detection Framework based on Approximate k-Nearest Neighbor Graphs [article]

Yi-Wei Liu, Hao Chen
2022 arXiv   pre-print
The test statistic we consider incorporates a useful pattern for moderate- to high- dimensional data so that the proposed method could detect various types of changes in the sequence.  ...  The time complexity of our proposed method is O(dn(log n+k log d)+nk^2) for an n-length sequence of d-dimensional data.  ...  On the other hand, the locations of the change-point detected by the two methods are very close, at about three quarters of the sequence.  ... 
arXiv:2006.13450v3 fatcat:zbuf2aj3ibh2lakmgiqtymxz5q

Optimal Distributed Optimization on Slowly Time-Varying Graphs [article]

Alexander Rogozin, César A. Uribe, Alexander Gasnikov, Nikolay Malkovsky, Angelia Nedić
2019 arXiv   pre-print
and χ̅ is a worst case bound on the condition number of the sequence of communication graphs.  ...  We provide a sufficient condition that guarantees a convergence rate with optimal (up lo logarithmic terms) dependencies on the network and function parameters if the network changes are constrained to  ...  Therefore, the Laplacian matrix of the graph changes as well, which defines a sequence of graph Laplacians { } ∞ =1 .  ... 
arXiv:1805.06045v6 fatcat:4pgeocp6h5ehzbj3ygdkkmduhi

Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach [article]

Isuru Udayangani Hewapathirana, Dominic Lee, Elena Moltchanova and Jeanette McLeod
2019 arXiv   pre-print
methods focus on changes in the behaviour of the overall network.  ...  In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes  ...  In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes  ... 
arXiv:1910.02301v1 fatcat:lsk3i3lq7jgj3a5d2gv5dg7efi

Highly Consistent Sequential Segmentation [chapter]

Michael Donoser, Martin Urschler, Hayko Riemenschneider, Horst Bischof
2011 Lecture Notes in Computer Science  
This matching step is based on a modified version of an efficient partial shape matching method which allows identification of similar parts of regions despite topology changes like merges and splits.  ...  We first introduce a segmentation method that uses results of the previous frame as initialization and significantly improves consistency in comparison to a single frame based approach.  ...  Fig. 3 . 3 Direct comparison on Weizmann sequence to most related methods of [12, 3] .  ... 
doi:10.1007/978-3-642-21227-7_5 fatcat:t4hlj3kjuzgtphmhft6252geou

Page 204 of American Antiquity Vol. 25, Issue 2 [page]

1959 American Antiquity  
Note that the occurrence of a seriation line on the graph is in itself a check on the method. If such a line may be drawn it indi- cates that there is a shift in one direction.  ...  A line is drawn through the scattered points so that there are as many points on one side of the line as the other.  ... 

Change Detection in Dynamic Attributed Networks [article]

Isuru Udayangani Hewapathirana
2020 arXiv   pre-print
We categorize these methods based on the levels of structure in the graph that are exploited to detect changes. These levels are vertices, edges, subgraphs, communities and the overall graph.  ...  It is a challenging problem because it involves a time sequence of attributed graphs, each of which is usually very large and can contain many attributes attached to the vertices and edges, resulting in  ...  Roger Jarquin from the School of Mathematics and Statistics, University of Canterbury, New Zealand, for providing insight and expertise that greatly assisted this work.  ... 
arXiv:2001.04734v1 fatcat:ohzwafwe5bfe5i4ox6uvlnn6au

Bottom-up and top-down brain functional connectivity underlying comprehension of everyday visual action

S. J. Hanson, C. Hanson, Y. Halchenko, T. Matsuka, A. Zaimi
2007 Brain Structure and Function  
New methods incorporating structural equation modeling of the data yielded distinct patterns of interactivity among brain areas as a function of the degree to which bottom-up and top-down data were available  ...  Making sense of a dynamic visual world involves perceiving streams of activity as discrete units such as eating breakfast or walking the dog.  ...  in one second bins resulting in a temporal response density (TRD) indexing an instantaneous estimate of change point in the action sequence.  ... 
doi:10.1007/s00429-007-0160-2 pmid:17968590 fatcat:7bkondcpkzhrxno64culaxoukq

TACO produces robust multisample transcriptome assemblies from RNA-seq

Yashar S Niknafs, Balaji Pandian, Hariharan K Iyer, Arul M Chinnaiyan, Matthew K Iyer
2016 Nature Methods  
High-throughput RNA sequencing (RNA-Seq) has enabled a deep understanding of the transcriptome 1-3 .  ...  While efforts to annotate high fidelity gene models by manual and automated systems have relied primarily on low-throughput sequencing methods 4-6 , several studies using RNA-Seq have described an expansive  ...  The MWU compares the expression values only at points of change on either side of a potential change point. ▪ E.g., if the expression on either side of the potential change point are X 1 = [15, 15, 15,  ... 
doi:10.1038/nmeth.4078 pmid:27869815 pmcid:PMC5199618 fatcat:qvhouovwg5azpmf62nnn6zriqu

A spectral approach to learning structural variations in graphs

Bin Luo, Richard C. Wilson, Edwin R. Hancock
2006 Pattern Recognition  
We commence by using correspondence information to place the nodes of each of a set of graphs in a standard reference order.  ...  This paper shows how to construct a linear deformable model for graph structure by performing principal components analysis (PCA) on the vectorised adjacency matrix.  ...  Due to changes in expression, the relationships between the feature points changes and hence they give rise to different graph structures.  ... 
doi:10.1016/j.patcog.2006.01.001 fatcat:cg25sm4xbzen7oe4g5smd4c5gq
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