Detecting communities in time-evolving/dynamic networks is an important operation used in many real-world network science applications. While there have been several proposed strategies for dynamic community detection, such approaches do not necessarily take advantage of the locality of changes. In this paper, we present a new technique called Delta-Screening (or simply, ∆-screening) for updating communities in a dynamic graph. The technique assumes that the graph is given as a series of time steps, and outputs a set of communities for each time step. At the start of each time step, the ∆-screening technique examines all changes (edge additions and deletions) and computes a subset of vertices that are likely to be impacted by the change (using the modularity objective). Subsequently, only the identified subsets are processed for community state updates. Our experiments demonstrate that this scheme, despite its ability to prune vertices aggressively, is able to generate significant savings in runtime performance (up to 38× speedup over static baseline and 5× over dynamic baseline implementations), without compromising on the quality. We test on both real-world and synthetic network inputs containing both edge additions and deletions. The ∆-screening technique is generic to be incorporated into any of the existing modularity-optimizing clustering algorithms. We tested using two state-of-the-art clustering implementations, namely, Louvain and SLM. In addition, we also show how to use the ∆-screening approach to delineate appropriate intervals of temporal resolutions at which to analyze a given input network.
Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs.In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called ∆-screening, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3× speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network.
Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was Correspondence to: Mingon Kang. This paper is a revised and expanded version of a paper entitled 'Integrative gene regulatory network inference using multi-omics data' presented at the
Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called ‘multi-omics data’, that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN’s capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.
[Purpose] The purpose of this study is to examine patient preferences for counseling related to sexuality post-stroke in Korea. [Subjects and Methods] A survey was conducted on 200 stroke patients. Among the 200 submitted questionnaires, 156 responded but 147 cases are available. The questionnaire is composed of 27 questions such as 8 independent variables related to the general characteristics of the patients, 7 dependent variables in conjunction with sexual intercourse and changed muscle tone, 6 questions regarding to changed sexual function, and 6 questions about a changed motor and a sensory function after stroke. To analyze the factors related to a sexual function after a stroke, we used the random forest, boosting algorithm and MANOVA. [Results] The most important variable in variable group 1 is VAR1, and then VAR22, VAR23, VAR26, VAR27, VAR25, VAR21 and VAR 24 respectively. The most important variable in variable group 2 is VAR22, and then VAR26, VAR23, VAR25, VAR1, VAR27, VAR21 and VAR 24. Finally, for variable group 3, VAR1 has the most important percentage, and we have the order as VAR26, VAR23, VAR27, VAR22, VAR25, VAR21 and VAR 24 among the rest of variables. The result of variable importance in boosting algorithm is somehow the same as that of random forest. [Conclusion] As a result of our analysis, we figured out that duration of illness, age, and education level are important factors of sexual functions for Korean Stroke patients.
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