Graph-based Community Detection in Social Network

Phyu, Kaing Sabai; Min, Myat Myat

doi:10.1109/icis46139.2019.8940245

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…large distance) will be given a smaller weight: . We then apply Louvain graph-based clustering to the resulting weighted graph (Phyu and Myat Min 2019).…”

Section: Spatially Informed Clusteringmentioning

confidence: 99%

Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities

et al. 2021

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Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and non-uniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial auto-correlation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell-cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomics data. We applied MERINGUE to a variety of spatially resolved transcriptomics datasets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-Seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomics data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease. Cold Spring Harbor Laboratory Press on May 30, 2021 -Published by genome.cshlp.org Downloaded from tissue, thereby losing valuable spatial context (Crosetto et al. 2015). Thus, how these subpopulations of cells are organized in space and how they may interact with each other remains an open question in many systems.To preserve informative spatial context, recent advances in imaging-based approaches have enabled in situ, spatially resolved transcriptomic profiling with single-cell resolution (Zhuang 2021). In addition, approaches based on spatially resolved RNA capture followed by sequencing, such as spatial transcriptomics and Slide-seq provide spatially resolved, untargeted transcriptomic profiling at the pixel level, with pixel size of 10-100µm (Larsson et al. 2021). Such high throughput data generation, both in terms of the number of genes and number of cells assayed, demands scalable computational methods that take advantage of this new spatial dimension to efficiently identify statistically significant spatial patterns and relationships. In addition, as these methods are applied to increasingly complex tissues, statistical analyses must be able to accommodate the non-uniform cell density induced by biological factors, such as the presence of multiple, often spatially organized, cell-types inherent to tissues, as well as technical factors, such as distortions from tissue sectioning.Three statistical methods, SpatialDE, Trendsceek, and SPARK have previously been developed to identify spatial gene expression heterogeneity, defined as an uneven, aggregated or patterned, spatial distribution of gene expression magnitudes (Svensson et al. 2018;Edsgärd et al. 2018;Sun et al. 2020).Briefly, SpatialDE identifies spatial gene expression heterogeneity by decomposing a gene's expression variance into a spatial and a non-spatial c...

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“…large distance) will be given a smaller weight: . We then apply Louvain graph-based clustering to the resulting weighted graph (Phyu and Myat Min 2019).…”

Section: Spatially Informed Clusteringmentioning

confidence: 99%

Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities

et al. 2021

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“…In this approach, the internal number of edges in the community is very high. 9,10 An approach of graph partitioning is investigating the relationship between the graph structure and clustering effectiveness, and developing a heuristic partitioning algorithm that is suitable for modularitybased algorithms. 11 Another approach of Graph partitioning is applied for finding overlapping communities by the Split betweenness (e.g., CONGA algorithm).…”

Section: Graph Partitioning Methodsmentioning

confidence: 99%

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Bakhthemmat

Izadi

2021

Big Data

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Community detection in social networks is one of the advertising methods in electronic marketing. One of the approaches to find communities in large social networks is to use greedy methods, because these methods perform very fast. Greedy methods are generally designed based on local decisions; thus, inappropriate local decisions may result in an improper global solution. The use of a greedy improved index with a futuristic approach can, to some extent, prevent inappropriate local choices. Our proposed method determines the influential nodes in the social network based on the followers and following and new futuristic greedy index. It classifies the nodes based on the influential nodes by the density-based clustering algorithm with a new distance function. The proposed method can improve clustering precision to detect communities by the futuristic greedy approach. We implemented the proposed algorithm with the map-reduce technique in the Hadoop structure. Experimental results in datasets show that the average of the rand index of clusters was accomplished by 99.32% in the proposed method. In addition, these results illustrate that there is a reduction in execution time by the proposed algorithm.

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“…In this context, there are many problems dealing with graph-based algorithms: community detection 53 or interaction 54 and recommendation systems. 55 A good collection of benchmarks in this context can be found on the website Stanford large network dataset collection.…”

Section: Social Networkmentioning

confidence: 99%

“…In this context, there are many problems dealing with graph-based algorithms: community detection 53 or interaction 54 and recommendation systems 55 …”

Section: Applications and Datasetsmentioning

confidence: 99%

Graphs in pattern recognition: successes, shortcomings, and perspectives

Conte

2023

J. Electron. Imag.

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Graphs in pattern recognition have been studied since the late seventies with alternating events of successes and failures. Far from wanting to propose a unique survey on the subject, we aim to present the areas in which graphs have been shown to be effective and to illustrate the problems still open in this research domain, with a focus on recent developments moving in the direction of deep learning. This work intends to arouse the interest of neophytes to this interesting domain that is the representation of the world through graphs.

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Graph-based Community Detection in Social Network

Cited by 6 publications

References 7 publications

Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities

Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities

Communities Detection for Advertising by Futuristic Greedy Method with Clustering Approach

Graphs in pattern recognition: successes, shortcomings, and perspectives

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