Detecting changes in time series of network graphs using minimum mean squared error and cumulative summation

Pincombe, Brandon

doi:10.21914/anziamj.v48i0.62

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…There are many models can be used to build time series models for dynamic networks, such as autoregressive models, Bayesian models, and Exponential‐family Random Graph Models (ERGM) . We use the autoregressive model as a representative for illustration.…”

Section: Learning Methodologiesmentioning

confidence: 99%

“…We use the autoregressive model as a representative for illustration. In Pincombe's project, the author proposed to use an AR moving‐average (ARMA) model, a variant of the AR model, to detect anomalies on dynamic communication networks. In this study, each observation on a graph topology, such as weight and diameter, is used to construct a single time series.…”

Section: Learning Methodologiesmentioning

confidence: 99%

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Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Xiao

Bertino

2017

WIREs Data Min & Knowl

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Deceptive engagement in social media, such as spamming, commenting, or rating with automatic scripts, spreading fabricated facts, seriously affects users’ trust on online services. Given the large volumes of information generated by users, effectively spotting users involved such deceptive engagement has become a challenging problem. Recent research has shown that techniques for analyzing temporal behavioral patterns are critical to address such problem. In this study, we survey recent advances in these techniques. We first summarize three approaches to model temporal information. Then, by using representative application examples, we discuss recent approaches with respect to their applications to real‐world large‐scale social media. With a focus on the temporal perspective, we then discuss advantages and challenges of each approach. WIREs Data Mining Knowl Discov 2017, 7:e1210. doi: 10.1002/widm.1210 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Prediction

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Section: Learning Methodologiesmentioning

confidence: 99%

Section: Learning Methodologiesmentioning

confidence: 99%

Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Xiao

Bertino

2017

WIREs Data Min & Knowl

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“… X = the number of bootstraps for which d b < d , where d is the difference between the maximum and minimum value of the original series, and d b is the difference between the maximum and minimum values generated by the bootstrap CUSUM samples (Taylor 2002; Pincombe 2007). N = the number of bootstraps.…”

Section: Methodsmentioning

confidence: 99%

Detecting change in an Australian flowering record: Comparisons of linear regression and cumulative sum analysis change point analysis

Keatley

Hudson

2012

Austral Ecology

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Linear regression and cumulative sum analysis (CUSUM) change point analyses were used to determine whether there had been a significant change in the first flowering date between 1983 and 2006 for 65 species. Both methods agreed that the first flowering date of 47 species did not change and that eight species had a significant change (P < 0.05) in their flowering. Three species shifted to later flowering and five species to earlier. Over the observation period, each method found that the average shift to later flowering was greater (37.4 days or approx. 1.56 days per year for CUSUM change point analysis and 51.4 days or 2.14 days per year for linear regression) than that to earlier flowering (28.4 days or approx. 1.20 days per year for change point analysis and 46.5 days or 1.97 days per year for linear regression). For the remaining 10 species the results of linear regression and change point methods differed. Each method found five species (three earlier flowering and two later) to have a significantly changed first flowering date over their observation period, where the other method did not. Some of these differences can be attributed to the fact that the CUSUM method can detect multiple change points whereas linear regression can not. Significant change points in first flowering date were identified for 13 species between the years 1987 to 1998. The most frequent year identified as a change point year was 1995.The two methods, although not interchangeable, had strong agreement (84.6%) in detecting shifts. This gives greater confidence that a change in flowering has occurred for eight species and equally importantly, that no change in first flowering date has occurred for 47 species.

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“…This GDD metric can be used to compare two structural brain networks, a structural brain network with every time-instant temporal network and, at the same time, as a proper metric for comparing two sets of brain networks via J index. (c) Spectral K distance (Pincombe, 2007), (d) Laplacian energy (Gutman and Zhou, 2006). This set of four spectral distance metrics implemented in our module can be used in conjunction with the J index.…”

Section: Graph-diffusion Distancementioning

confidence: 99%

Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

Marimpis

Dimitriadis

Goebel

2021

Human Brain Mapping

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Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For the last decade, researchers have been modelling brain structure and function via a graph or network that comprises brain regions that are either anatomically connected via tracts or functionally via a more extensive repertoire of functional associations. Network neuroscience is a relatively new multidisciplinary scientific avenue of the study of complex systems by pursuing novel ways to analyze, map, store and model the essential elements and their interactions in complex neurobiological systems, particularly the human brain, the most complex system in nature. Due to a rapid expansion of neuroimaging data sets' size and complexity, it is essential to propose and adopt new empirical tools to track dynamic patterns between neurons and brain areas and create comprehensive maps. In recent years, there is a rapid growth of scientific interest in moving functional neuroimaging analysis beyond simplified group or time-averaged approaches and sophisticated algorithms that can capture the time-varying properties of functional connectivity. We describe algorithms and network metrics that can capture the dynamic evolution of functional connectivity under this perspective. We adopt the word 'chronnectome' (integration of the Greek word 'Chronos', which means time, and connectome) to describe this specific branch of network neuroscience that explores how mutually informed brain activity correlates across time and brain space in a functional way. We also describe how good temporal mining of temporally evolved dynamic functional networks could give rise to the detection of specific brain states over which our brain evolved. This characteristic supports our complex

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Detecting changes in time series of network graphs using minimum mean squared error and cumulative summation

Cited by 11 publications

References 19 publications

Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

Detecting change in an Australian flowering record: Comparisons of linear regression and cumulative sum analysis change point analysis

Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

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