Overlapping Communities Detection Based on Link Partition in Directed Networks

Many complex systems can be described as networks to comprehend both the structure and the function. Community structure is one of the most important properties of complex networks. Detecting overlapping communities in networks have been more attention in recent years, but the most of approaches to this problem have been applied to the undirected networks. This paper presents a novel approach based on link partition to detect overlapping communities structure in directed networks. In contrast to previous researches focused on grouping nodes, our algorithm defines communities as groups of directed links rather than nodes with the purpose of nodes naturally belong to more than one community. This approach can identify a suitable number of overlapping communities without any prior knowledge about the community in directed networks. We evaluate our algorithm on a simple artificial network and several real-networks. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in directed networks. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.330

A Topology-Based Algorithm for Directed Network Alignment

Network alignment has brought significant advances to our understanding of complex networks, for example, the Worldwide Web, biological networks, and social networks. Triangles comprised of three nodes are the simplest subnet in the directed network. The distribution of triangles is an important indicator of understanding the dynamics and function of directed networks. In this paper, we present a novel alignment algorithm for directed networks only based on topology structure, which can be used for any two networks. The transcriptional regulatory networks (TRNs) of E. coli and S. cerevisiae are used to evaluate the algorithm. Experimental results demonstrate that the algorithm proposed is efficient for aligning directed networks.DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.295

Identifying Overlapping Communities in Directed Networks via Triangles

A lot of complex systems in nature and society can be represented as the form of network. The small-scale subnets topological features are vital to understand the dynamics and function of the networks. Triangles comprised of three nodes are the simplest subnet in the network. Based on the triangle distribution of the complex network, we present a novel approach to detect overlapping community structure in directed networks. Different from previous studies focused on grouping nodes, our method defines communities as groups of links rather than nodes so that nodes naturally belong to more than one community. It can identify a suitable number of overlapping communities without any prior knowledge about the community. We evaluated our approach on several real-networks. Experimental results prove that the algorithm proposed is efficient for detecting overlapping communities in directed networks. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3401

Differential privacy protection technology and its application in big data environment

The privacy protection in big data is a research hotspot in the field of cyberspace security.As a strict and provable definition of privacy protection,studying application status of differential privacy protection in big data environment can provide reference and guidance for its subsequent system applications.Based on the analysis of the related concepts and technical characteristics of differential privacy protection,the application of differential privacy protection technology was reviewed in data distribution and analysis,cloud computing and big data computing,location and trajectory services and social networks,which expounded the current representative research results and analyzed its existing problems.The research shows that the existing results have made effective innovation and exploration of differential privacy protection applications from the aspects of differential privacy protection mechanism,noise addition mechanism and location,and data processing methods,and the related results have been cross-applied in different scenarios.Finally,four major problems that need to be studied in the further systematic application of differential privacy protection in the big data environment are proposed

Metric and classification model for privacy data based on Shannon information entropy and BP neural network

Aiming at the requirements of privacy metric and classification for the difficulty of private data identification in current network environment, a privacy data metric and classification model based on Shannon information entropy and BP neural network was proposed. The model establishes two layers of privacy metrics from three dimensions. Based on the dataset itself, Shannon information entropy was used to weight the secondary privacy elements, and the privacy of each record in the dataset under the first-level privacy metrics was calculated. The trained BP neural network was used to output the classification result of privacy data without pre-determining the metric weight. Experiments show that the model can measure and classify private data with low false rate and small misjudged deviation

Evolutionary game analysis of rural public–private partnership older adult care project in the context of population aging in China

IntroductionPublic–private partnership (PPP) older adult care project is an effective means to solve the dilemma of the aging population in China's rural areas, but there are some problems in the operation process, such as a low participation rate and poor service quality, resulting in the needs of rural older adult groups not being met.MethodsTo alleviate the pressure of the aging population in rural areas, this study establishes an evolutionary game model for the PPP older adult care project, then defines the interests of local government, the private sector, and rural older adult residents, based on which it discusses the strategic choices of the three parties in the evolutionary process, and finally analyzes the influencing factors of the strategic choices of the game parties through simulation.ResultsThe results suggest that whether the private sector chooses to actively participate in the project will be influenced by the willingness of local government and rural older adult residents to participate in the project. Local government could play the role of supervisor through reward and punishment mechanisms. Whether older rural residents choose to participate in the project depends on the number of benefits they would receive.DiscussionBased on these findings, local governments should clarify the responsibilities of relevant stakeholders, adopt a regulatory strategy combining subsidies and penalties, improve the participation efficiency of rural older adult residents, promote the effective operation of PPP older adult care projects, and improve the quality of rural older adult care services in the new era

Probabilistic Compute-in-Memory Design For Efficient Markov Chain Monte Carlo Sampling

Markov chain Monte Carlo (MCMC) is a widely used sampling method in modern artificial intelligence and probabilistic computing systems. It involves repetitive random number generations and thus often dominates the latency of probabilistic model computing. Hence, we propose a compute-in-memory (CIM) based MCMC design as a hardware acceleration solution. This work investigates SRAM bitcell stochasticity and proposes a novel ``pseudo-read'' operation, based on which we offer a block-wise random number generation circuit scheme for fast random number generation. Moreover, this work proposes a novel multi-stage exclusive-OR gate (MSXOR) design method to generate strictly uniformly distributed random numbers. The probability error deviating from a uniform distribution is suppressed under $10^{-5}$. Also, this work presents a novel in-memory copy circuit scheme to realize data copy inside a CIM sub-array, significantly reducing the use of R/W circuits for power saving. Evaluated in a commercial 28-nm process development kit, this CIM-based MCMC design generates 4-bit$\sim$32-bit samples with an energy efficiency of $0.53$~pJ/sample and high throughput of up to $166.7$M~samples/s. Compared to conventional processors, the overall energy efficiency improves $5.41\times10^{11}$ to $2.33\times10^{12}$ times

Current advances on single or multi-omics analysis of esophageal cancer

Esophageal cancer is associated with high mortality rates and is one of the cancers with the worst prognosis. Its incidence has significant regional specificity, particularly in China where it is much higher than in other countries. Moreover, effective diagnostic markers, therapeutic targets, and molecular subtyping biomarkers are currently lacking for esophageal cancer. Nevertheless, large-scale omics studies have identified dozens of robust genetic risk loci and prognosis-related loci, drawn genomic, epigenomic, and transcriptomic maps of esophageal cancer at multiple molecular levels, and described significant differences between esophageal squamous cell carcinoma and adenocarcinoma. These studies are of great significance for exploring the occurrence and development mechanism of esophageal cancer, guiding clinical treatment, and improving patient prognosis. This review, from the perspective of multi-omics, discusses the analytical strategies employed in these studies and summarizes their core findings. It emphasizes that the integration and analysis of multi-omics data is a key focus and development trend in the precise medical research of esophageal cancer, and has broad research and application prospects.Keywords: Esophageal cancer; GWAS; Precision medicine; Biomarker 

Meta-analysis of the effect and clinical significance of Delphian lymph node metastasis in papillary thyroid cancer

ObjectiveTo investigate the effect and clinical significance of Delphian lymph nodes (DLN) on the factors influencing papillary thyroid cancer (PTC) to provide individualized guidance for the surgical treatment of thyroid cancer.MethodsRelevant studies from PubMed, Web of Science, the Cochrane Library, Embase, and China National Knowledge Infrastructure databases were searched until February 13, 2023. Stringent selection parameters were used to obtain included data and homogeneous articles. Analyses were performed using Revman 5.4 and SPSS software. A P-value of < 0.05 was considered statistically significant.ResultsFive studies were finally included in this study. The results revealed a higher risk of DLN metastasis (DLNM) in patients with tumor size >1cm, multifocality, and extrathyroidal extension (ETE) of the thyroid. The risk of central lymph node metastasis (CLNM) was 11.25 times higher in DLN-positive patients with PTC than in DLN-negative (OR = 11.25, 95% CI: 8.64–14.64, P < 0.05) patients. The risk of LLNM was 5.57 times higher in DLN-positive patients with PTC than in DLN-negative (OR = 5.57, 95% CI: 4.57–6.78, P < 0.001) patients. The risk of postoperative recurrence in DLN-positive patients with PTC was 3.49 times higher (OR = 3.49, 95% CI: 1.91–6.38, P < 0.001) than in DLN-negative patients with PTC.ConclusionPatients with tumor size >1 cm in diameter, multifocality, and ETE have an increased risk for DLN development. DLN-positive patients with central and lateral cervical lymph node metastasis and postoperative recurrence are at higher risk than DLN-negative patients