Prioritizing network communities

Žitnik, Marinka; Sosič, Rok; Leskovec, Jure

doi:10.1038/s41467-018-04948-5

Cited by 58 publications

(50 citation statements)

References 80 publications

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“…Examples include the Erdos-Renyi, small-world and scale-free models. The parameters of these models can in turn be used to characterize network structure [1], [2]. Moreover, these parametric characterizations implicitly represent the key structural mechanisms determining the global features of a network [3].…”

Section: Introductionmentioning

confidence: 99%

Graph Motif Entropy for Understanding Time-Evolving Networks

Chen

Bai

Wang

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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The structure of networks can be efficiently represented using motifs, which are those subgraphs that recur most frequently. One route to understanding the motif structure of a network is to study the distribution of subgraphs using statistical mechanics. In this paper, we address the use of motifs as network primitives using the cluster expansion from statistical physics. By mapping the network motifs to clusters in the gas model, we derive the partition function for a network and this allows us to calculate global thermodynamic quantities, such as energy and entropy. We present analytical expressions for the numbers of certain types of motifs, and compute their associated entropy. We conduct numerical experiments for synthetic and real-world data-sets and evaluate the qualitative and quantitative characterizations of the motif entropy derived from the partition function. We find that the motif entropy for real-world networks, such as financial stock market networks, is sensitive to the variance in network structure. This is in line with recent evidence that network motifs can be regarded as basic elements with well defined information-processing functions.

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Section: Introductionmentioning

confidence: 99%

Graph Motif Entropy for Understanding Time-Evolving Networks

Chen

Bai

Wang

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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“…The application of cell searching is not limited to mapping cells between different data sets. The task of finding similar cells within a data set is a subroutine in many analysis methods, such as data smoothing [21], clustering [35], community detection [36], and visualization [37]. As we have demonstrated in the self-mapping experiment, our LSH-based method can find similar cells within a data set with high accuracy and throughput; thus, it would be possible to speed up analysis by utilizing our cell search method in lieu of the currently available method.…”

Section: Discussionmentioning

confidence: 99%

CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing

et al. 2019

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Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1600 cells per second.Electronic supplementary materialThe online version of this article (10.1186/s13059-019-1639-x) contains supplementary material, which is available to authorized users.

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“…The application of cell searching is not limited to mapping cells between different data sets. The task of finding similar cells within a data set is a subroutine in many analysis methods, such as data smoothing [20], clustering [35], community detection [36], and visualization [37]. As we have demonstrated in the selfmapping experiment, our LSH-based method can find similar cells within a data set with high accuracy and throughput; thus, it would be possible to speed up Plus and minus signs in the index column denote the index search and the linear search, respectively.…”

Section: Discussionmentioning

confidence: 99%