Community Detection With Contextual Multilayer Networks

Ma, Zongming; Nandy, Sagnik

doi:10.1109/tit.2023.3238352

Cited by 13 publications

(5 citation statements)

References 25 publications

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“…While AMP algorithms have been proposed for generalised linear models [15][16][17][18]27] and distributed learning [46,47], developing models for these tasks using genomic data requires a lot of further algorithm development and benchmarking. However, given the speed and accuracy of our approach, a wider range of statistical models may now be feasible for both large-scale sequence and multi-omics data.…”

Section: Discussionmentioning

confidence: 99%

Joint modelling of whole genome sequence data for human height via approximate message passing

Depope,

Bajzik,

Mondelli

et al. 2023

Preprint

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Efficient utilization of large-scale biobank data is crucial for inferring the genetic basis of disease and predicting health outcomes from the DNA. Yet we lack accurate methods that scale to data where electronic health records are linked to whole genome sequence information. To address this issue, we develop a new algorithmic paradigm based on Approximate Message Passing (AMP), which is specifically tailored for genomic prediction and association testing. Our method yields comparable out-of-sample prediction accuracy to the state of the art on UK Biobank traits, whilst dramatically improving computational complexity, with a 10x-speed up in the run time. In addition, AMP theory provides a joint association testing framework, which outperforms the commonly used REGENIE method, in a third of the compute time. This first, truly large-scale application of the AMP framework lays the foundations for a far wider range of statistical analyses for hundreds of millions of variables measured on millions of people.

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Section: Discussionmentioning

confidence: 99%

Joint modelling of whole genome sequence data for human height via approximate message passing

Depope,

Bajzik,

Mondelli

et al. 2023

Preprint

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show abstract

“…Some works consider the diversity of connections or relationships between nodes and model them as multilayer networks, where each type of connection is treated as a network layer [27]. Therefore, it has given rise to several complementary models for multi-layer SBMs [28]- [32]. Based on the underlying community structure, a collection of graphs are generated on the same set of nodes with identical latent community labels.…”

Section: B Related Workmentioning

confidence: 99%

“…Multi-layer networks tend to capture the dynamic changes or hierarchical structures of networks, such as the evolving community structure in social networks over time [33] or the multi-level modular organization in biological networks [34]. Several variants have been explored, but typically, given the community labels, the layers are conditionally independent [28], [30]- [32], [35]. On the other hand, the approach in this paper is to propose a probabilistic model for detecting the community structure of from multiple correlated graphs, where the graphs refer to multiple graphical representations obtained from different perspectives or modalities, which are used to handle network data from diverse data sources or with different features.…”

Section: B Related Workmentioning

confidence: 99%

Community Detection of Complex Networks Based on Advanced `Stochastic Block Model

Zhang

Liu

et al. 2022

Proceedings of the 5th International Conference on Big Data Technologies

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This paper considers the problem of community detection on multiple potentially correlated graphs from an information-theoretical perspective. We first put forth a random graph model, called the multi-view stochastic block model (MVSBM), designed to generate correlated graphs on the same set of nodes (with cardinality n). The n nodes are partitioned into two disjoint communities of equal size. The presence or absence of edges in the graphs for each pair of nodes depends on whether the two nodes belong to the same community or not. The objective for the learner is to recover the hidden communities with observed graphs. Our technical contributions are two-fold: (i) We establish an information-theoretic upper bound (Theorem 1) showing that exact recovery of community is achievable when the model parameters of MVSBM exceed a certain threshold. (ii) Conversely, we derive an informationtheoretic lower bound (Theorem 2) showing that when the model parameters of MVSBM fall below the aforementioned threshold, then for any estimator, the expected number of misclassified nodes will always be greater than one. Our results for the MVSBM recover several prior results for community detection in the standard SBM as well as in multiple independent SBMs as special cases.

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“…Many complex systems in reality can be represented as network structures [1], such as social networks, protein interaction networks, the computer internet, and biological disease transmission networks [2]. Network nodes symbolize entities, whereas linked edges symbolize the relationships between these things.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

Huang,

Zhong

2024

Mathematics

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Community structure is a significant characteristic of complex networks, and community detection has valuable applications in network structure analysis. Non-negative matrix factorization (NMF) is a key set of algorithms used to solve the community detection issue. Nevertheless, the localization of feature vectors in the adjacency matrix, which represents the characteristics of complex network structures, frequently leads to the failure of NMF-based approaches when the data matrix has a low density. This paper presents a novel algorithm for detecting sparse network communities using non-negative matrix factorization (NMF). The algorithm utilizes local feature vectors to represent the original network topological features and learns regularization matrices. The resulting feature matrices effectively reveal the global structure of the data matrix, demonstrating enhanced feature expression capabilities. The regularized data matrix resolves the issue of localized feature vectors caused by sparsity or noise, in contrast to the adjacency matrix. The approach has superior accuracy in detecting community structures compared to standard NMF-based community detection algorithms, as evidenced by experimental findings on both simulated and real-world networks.

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Community Detection With Contextual Multilayer Networks

Cited by 13 publications

References 25 publications

Joint modelling of whole genome sequence data for human height via approximate message passing

Joint modelling of whole genome sequence data for human height via approximate message passing

Community Detection of Complex Networks Based on Advanced `Stochastic Block Model

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

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