Local community detection based on network motifs

Zhang, Yunlei; Wu, Bin; Liu, Yu; Lv, Jinna

doi:10.26599/tst.2018.9010106

Cited by 25 publications

(22 citation statements)

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“…In this section, we propose a community-based algorithm for acceleration, since the advanced greedy algorithm in Section 4 still requests a high time consumption. The community structure is valuable and fundamental in social networks [29] , [30] , as users in the same community influence each other much more than users in different communities [31] . Therefore, it is reasonable to estimate the impact in the whole network by the impact in a community, thus reducing the time consumption.…”

Section: Community-based Algorithmmentioning

confidence: 99%

Susceptible user search for defending opinion manipulation

Tang

Tian

Zheng

et al. 2021

Future Generation Computer Systems

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The development of cyberspace offers unprecedentedly convenient access to online communication, thus inducing malicious individuals to subtly manipulate user opinions for benefits. Such malicious manipulations usually target those influential and susceptible users to mislead and control public opinion, posing a bunch of threats to public security. Therefore, an intelligent and efficient searching strategy for targeted users is one prominent and critical approach to defend malicious manipulations. However, the major body of current studies either provide solutions under ideal scenarios or offer inefficient solutions without guaranteed performance. As a result, this work adopts the combination of unsupervised learning and heuristic search to discover susceptible and key users for defense. We first propose a greedy algorithm fully considering the susceptibilities of different users, then adopt unsupervised learning and utilize the community property to design an accelerated algorithm. Moreover, the approximation guarantees of both greedy and community-based algorithms are systematically analyzed for some practical circumstances. Extensive experiments on real-world datasets demonstrate that our algorithms significantly outperform the state-of-the-art algorithm.

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Section: Community-based Algorithmmentioning

confidence: 99%

Susceptible user search for defending opinion manipulation

Tang

Tian

Zheng

et al. 2021

Future Generation Computer Systems

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“…Spectral clustering algorithms convert a given set of objects into a set of points in multi-dimensional space, and the coordinates of these points are feature vector elements. This conversion reveals the hidden attributes of the initial dataset, and is either conducted as a clustering operation (Zhang et al 2019; Zhou and Amini 2019) or as a partitioning operation (Carusi and Bianchi 2019). Optimization algorithms employ a local fitness function or community structure evaluation function as the objective function in an optimization process conducted using various optimization algorithms, such as simulated annealing and genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

Community detection based on similarities of communication behavior in IP networks

Zhang

Zhou

et al. 2020

J Ambient Intell Human Comput

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Communities are an important feature of real-world networks that can reveal the structure and dynamic characteristics of networks. Accordingly, the accurate detection and analysis of the community structure in large-scale IP networks is highly beneficial for their optimization and security management. This paper addresses this issue by proposing a novel community detection method based on the similarity of communication behavior between IP nodes, which is determined by analyzing the communication relationships and frequency of interactions between the nodes in the network. On this basis, the nodes are iteratively added to the community with the highest similarity to form the final community division result. The results of experiments involving both complex public network datasets and real-world IP network datasets demonstrate that the proposed method provides superior community detection performance compared to that of four existing state-of-the-art community detection methods in terms of modularity and normalized mutual information indicators.

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“…SAME-clustering [26] combined a maximally diverse subset of four clustering solutions obtained from five individual clustering methods, then the subset was combined with the expectation-maximization (EM) algorithm to build an ensemble clustering solution. Among all these methods, we find that hierarchical clustering [10,15,16,18,25,[27][28][29] and graph-based clustering [30][31][32][33][34] such as spectral clustering and Louvain community detection algorithm are the most popular approaches in the downstream clustering analysis [9,12,[21][22][23][24]35] . Additionally, densitybased clustering is also widely used in scRNA-seq data analysis for the identification of outlier cells [36,37] .…”

Section: Introductionmentioning

confidence: 99%

A data-driven clustering recommendation method for single-cell RNA-sequencing data

Tian

Zheng

Liang

et al. 2021

Tsinghua Sci. Technol.

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Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it possible to solve biological problems at the single-cell resolution. One of the critical steps in cellular heterogeneity analysis is the cell type identification. Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters. Among all the methods, hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning, dropout imputation, and dimensionality reduction. In this study, we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions. The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension, while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension. Motivated by this finding, a new strategy, called QRS, is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not. Finally, a data-driven clustering recommendation method, called DDCR, is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data. We perform DDCR on two typical single cell clustering methods, SC3 and RAFSIL, and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

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Local community detection based on network motifs

Cited by 25 publications

References 0 publications

Susceptible user search for defending opinion manipulation

Susceptible user search for defending opinion manipulation

Community detection based on similarities of communication behavior in IP networks

A data-driven clustering recommendation method for single-cell RNA-sequencing data

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