What network motifs tell us about resilience and reliability of complex networks

Dey, Asim Kumer; Gel, Yulia R.; Poor, H. Vincent

doi:10.1073/pnas.1819529116

Cited by 74 publications

(27 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Identifying and describing communities within broader networks is an active research area in SNA [16]. In network analysis, a community refers to "groups of vertices which probably share common properties and/or play similar roles within the graph" [17].…”

Section: B Community Detectionmentioning

confidence: 99%

Network positions in active learning environments in physics

Traxler

Suda

Brewe

et al. 2020

Phys. Rev. Phys. Educ. Res.

View full text Add to dashboard Cite

This study uses positional analysis to describe the student interaction networks in four research-based introductory physics curricula. Positional analysis is a technique for simplifying the structure of a network into blocks of actors whose connections are more similar to each other than to the rest of the network. This method describes social structure in a way that is comparable between networks of different sizes and densities and can show large-scale patterns such as hierarchy among positions. We detail one positional analysis method and apply it to class sections of Peer Instruction, SCALE-UP, ISLE, and Minnesota Model context-rich problems. At the level of detail shown in the blockmodels, most of the curricula are more alike than different, showing a late-term tendency to form coherent subgroups that communicate actively among themselves but have few interposition links. Initial position assignments tend to change from beginning to end of the term, but in cases where the initial assignment is stable, those students appear to become more connected to each other and to the largest network component. These trends in position structure and stability may be network signatures of active learning classes, but wider data collection is needed to investigate.

show abstract

Section: B Community Detectionmentioning

confidence: 99%

Network positions in active learning environments in physics

Traxler

Suda

Brewe

et al. 2020

Phys. Rev. Phys. Educ. Res.

View full text Add to dashboard Cite

show abstract

“…Moreover, we intend to extend our analysis to include the estimation of network summaries that are based on the local topology and geometry of the graph. In particular, we intend to incorporate a motif-based analysis (Milo et al, 2002; Dey et al, 2019; Sarkar et al, 2019) and the concepts of topological data analysis (TDA), particularly, persistent homology, in the derivation of graph summary statistics (Carlsson, 2009; Patania et al, 2017; Carlsson, 2019). Indeed, tracking local network topological summaries based on graph persistent homology offers multi-fold benefits.…”

Section: Resultsmentioning

confidence: 99%

Nonparametric Anomaly Detection on Time Series of Graphs

Ofori-Boateng

Gel

Cribben

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Identifying change points and/or anomalies in dynamic network structures has become increasingly popular across various domains, from neuroscience to telecommunication to finance. One of the particular objectives of the anomaly detection task from the neuroscience perspective is the reconstruction of the dynamic manner of brain region interactions. However, most statistical methods for detecting anomalies have the following unrealistic limitation for brain studies and beyond: that is, network snapshots at different time points are assumed to be independent. To circumvent this limitation, we propose a distribution-free framework for anomaly detection in dynamic networks. First, we present each network snapshot of the data as a linear object and find its respective univariate characterization via local and global network topological summaries. Second, we adopt a change point detection method for (weakly) dependent time series based on efficient scores, and enhance the finite sample properties of change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap. We apply our method to simulated and to real data, particularly, two functional magnetic resonance imaging (fMRI) datasets and the Enron communication graph. We find that our new method delivers impressively accurate and realistic results in terms of identifying locations of true change points compared to the results reported by competing approaches. The new method promises to offer a deeper insight into the large-scale characterizations and functional dynamics of the brain and, more generally, into intrinsic structure of complex dynamic networks.

show abstract

“…A motif is a recurrent multi-node subgraph pattern. A detailed description of network motifs and their functionality in a complex network can be found in [51] , [52] , [53] , [54] , and [55] . Fig.…”

Section: Data and Variablesmentioning

confidence: 99%