Phase transition of Surprise optimization in community detection

Ju, Xu; Tang, Yanni; Gao, Yuanyuan; Yi, Hao; Li, Jianming; Zhang, Yan; Shi, Chen

doi:10.1016/j.physa.2017.09.090

Cited by 11 publications

(6 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our method can effectively improve the structure of original networks to enhance the ability of network embedding algorithms. Due to the improvement of network structure, some problems of other network analysis algorithms may also be solved or improved, such as the resolution limit in community detection [52,53]. Some studies have shown that network enhancement can mitigate the Frontiers in Physics frontiersin.org resolution limit and improve traditional community detection algorithms [35,54].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

MERP: Motifs enhanced network embedding based on edge reweighting preprocessing

Ju²,

Li³

et al. 2022

Front. Phys.

View full text Add to dashboard Cite

Network embedding has attracted a lot of attention in different fields recently. It represents nodes in a network into a low-dimensional and dense space while preserving the structural properties of the network. Some methods (e.g. motif2Vec, RUM, and MODEL) have been proposed to preserve the higher-order structures, i.e., motifs in embedding space, and they have obtained better results in some downstream network analysis tasks. However, there still exists a significant challenge because original motifs may include redundant noise edges, and embedding entire motifs into embedding space may adversely affect the performance in downstream tasks. To overcome this problem, we propose a motifs enhancement framework for network embedding, based on edge reweighting. Through edge reweighting, the weight of redundant noise edges between motifs is decreased. Therefore, the effect of redundant noise edges will be reduced in the embedding space. We apply the edge reweighting as a preprocessing phase in network embedding, and construct the motifs enhanced network by incorporating enhanced motifs structures with the original network. By doing this, the embedding vectors from the motifs enhanced network can achieve better performance in downstream network analysis tasks. Extensive experiments are performed on two network analysis tasks (community detection and node classification) with synthetic and real-world datasets. The results show that our framework outperforms state-of-the-art network embedding methods.

show abstract

Section: Conclusion and Discussionmentioning

confidence: 99%

MERP: Motifs enhanced network embedding based on edge reweighting preprocessing

Ju²,

Li³

et al. 2022

Front. Phys.

View full text Add to dashboard Cite

show abstract

“…For example, Disease Module Identification DREAM Challenge specifies a valid module size between 3 and 100 (Choobdar, et al, 2019). Furthermore, some module identification algorithms may split a large module into several small submodules, or aggregate several small modules into a large one, because of the resolution related to the intrinsic definition or mechanism of algorithms (Fortunato and Barthé lemy, 2007;Xiang, et al, 2018;Xiang, et al, 2017). In this case, algorithms with flexible resolution may more effectively mine the module structures of networks.…”

Section: /mentioning

confidence: 99%

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure

Meng

Zhao

et al. 2022

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

Motivation Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. Results We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM’s predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. Conclusions The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

show abstract

“…This means that the partition for x=1, 2 and 3 will be preferred in turn. Other statistical measures such original surprise and modularity have similar behaviors, but the critical points are different for different statistical measures [34].…”

Section: Critical Behavior Of Asymptotic Surprise and Its Resolutionmentioning

confidence: 99%

“…Many methods have been proposed to identify the communities in complex networks by various approaches [9][10][11][12][13][14][15][16][17][18], such as spectral analysis [18], random walk [19][20][21], dynamics [22][23][24][25], label propagation [26], and modularity optimization [27,28]. The existing methods could indeed help reveal intrinsic structures in the networks, but they also have respective scopes of application, and thus it is necessary to study their behaviors, e.g., the resolution in community detection [29][30][31][32][33][34][35]. This could help understand the methods themselves in depth and promote the development of community-detection methods.…”

Section: Introductionmentioning

confidence: 99%

Identifying multi-scale communities in networks by asymptotic surprise

Zhang

et al. 2019

J. Stat. Mech.

Self Cite

View full text Add to dashboard Cite

Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise, an asymptotic approximation of surprise. We discussed the critical behaviors of asymptotic surprise in phase transition of community partition theoretically. Then, according to the theoretical analysis, a multi-resolution method based on asymptotic surprise was introduced, which provides an alternative approach to study multi-scale communities in networks, and an improved Louvain algorithm was proposed to optimize the asymptotic surprise more effectively. By a series of experimental tests in various networks, we validated the critical behaviors of the asymptotic surprise further and the effectiveness of the improved Louvain algorithm, displayed its ability to solve the first-type resolution limit and stronger tolerance against the second-type resolution limit, and confirmed its effectiveness of revealing multi-scale community structures in multi-scale networks.

show abstract

Phase transition of Surprise optimization in community detection

Cited by 11 publications

References 60 publications

MERP: Motifs enhanced network embedding based on edge reweighting preprocessing

MERP: Motifs enhanced network embedding based on edge reweighting preprocessing

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure

Identifying multi-scale communities in networks by asymptotic surprise

Contact Info

Product

Resources

About