Change point detection in social networks using a multivariate exponentially weighted moving average chart

The multivariate exponentially weighted moving average (MEWMA) control chart, in which the smoothing parameter is a principal diagonal matrix, is established based on the assumption that the samples follow the standard normal distribution to monitor the multivariate quality characteristics. However, monitoring quality characteristics always exists in the whole process. To make the MEWMA control chart more effective, a modified multivariate exponentially weighted moving average control chart, namely practical MEWMA (PEWMA) control chart, is proposed, in which the smoothing coefficient matrix is extended to a full-rank matrix which the smoothing parameter are all not zero. Analysis of both the in-control and out-of-control statistical performance of PEWMA control chart illustrate that the average run length of PEMWA control chart is more superior to the MEWMA. Finally combined with a factory example, a minimisation cost model is constructed based on the Lorenzen-Vance economic model to optimise the parameter combination.

ABSTRACTCompared to the grid search approach to optimal design of control charts, the gradient-based approach is more computationally efficient as the gradient information indicates the direction to search the optimal design parameters. However, the optimal parameters of multivariate exponentially weighted moving average (MEWMA) control charts are often obtained by using grid search in the existing literature. Note that the average run length (ARL) performance of the MEWMA chart can be calculated based on a Markov chain model, making it feasible to estimate the ARL gradient from it. Motivated by this, this paper develops an ARL gradient-based approach for the optimal design and sensitivity analysis of MEWMA control charts. It is shown that the proposed method is able to provide a fast, accurate, and easy-to-implement algorithm for the design and analysis of MEWMA charts, as compared to the conventional design approach based on grid search.

ABSTRACTProfile monitoring is one of the new research areas in statistical process control. Most of the control charts in this area are designed with fixed sampling rate which makes the control chart slow in detecting small to moderate shifts. In order to improve the performance of the conventional fixed control charts, adaptive features are proposed in which, one or more design parameters vary during the process. In this paper the variable sample size feature of EWMA3 and MEWMA schemes are proposed for monitoring simple linear profiles. The EWMA3 method is based on the combination of three exponentially weighted moving average (EWMA) charts for monitoring three parameters of a simple linear profile separately and the Multivariate EWMA (MEWMA) chart is based on the using a single chart to monitor the coefficients and variance of a general linear profile. Also a two-sided control chart is proposed for monitoring the standard deviation in the EWMA3 method. The performance of the proposed charts is compared in terms of the average time to signal. Numerical examples show that using adaptive features increase the power of control charts in detecting the parameter shifts. Finally, the performance of the proposed variable sample size schemes is illustrated through a real case in the leather industry.

Water treatment is intended to change the original water quality that does not fulfill the health requirements become a water for human consumption and must comply with the levels of certain parameters. Quality control can be done by forming a Multivariate Exponentially Weighted Moving Average (MEWMA) control chart. In the Multivariate Exponentially Weighted Moving Average (MEWMA) control charts with λ = 0.25 and UCL = 13.92658 seen that process controlled statistically. Once the process is under control, it can be done analysis of the ability of the process to determine whether the process fulfill the specifications or not. In the calculation process capability univariate each characteristics and multivariate process capability index values obtained more than 1 means that the process is going well. Keywords : water quality, Multivariate Exponentially Weighted Moving Average (MEWMA), process capability.

ABSTRACTPurposeThis study examined how social network strength (i.e., the intensity and frequency of interactions within one's personal network, assessed using centrality measures such as degree, closeness, betweenness, and eigenvector centrality) affects mental health.MethodsThe study was conducted with 108 first‐year university students. Data were collected using a structured questionnaire, and NetMiner 4.0 was employed to analyze degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality as indicators of social network centrality. Additionally, Quadratic Assignment Procedure (QAP) correlation analysis and QAP regression analysis were conducted to assess the relationship between social network strength and mental health.ResultsThe study found that stronger social networks were linked to better mental health in late adolescence. Specifically, higher social network centrality was associated with lower levels of hopelessness and depression. QAP correlation analysis revealed that hopelessness had a significant negative correlation with in‐closeness centrality (which reflects how easily one can be reached by others within the network) (r = −0.252, p < 0.05), while depression was negatively correlated with in‐degree centrality (the number of direct incoming connections from peers) (r = −0.233, p < 0.05), in‐closeness centrality (r = −0.256, p < 0.05), and eigenvector centrality (a measure of how well a person is connected to popular or influential members of the network) (r = −0.291, p < 0.01). QAP regression analysis further confirmed that weaker social ties, indicated by higher out‐closeness centrality (how easily one can reach others, especially those at a distance), were associated with higher depressive symptoms (adj. R2 = 0.143, F = 2.423, p < 0.05). These results suggest that building stronger and more integrated social networks may help reduce psychological distress and support mental well‐being in late adolescence.ConclusionStronger social networks in late adolescence are associated with better mental health, highlighting the importance of social connections. While correlational findings suggest general links between centrality and emotional well‐being, the regression results underscore specific predictors with practical implications. In‐degree, in‐closeness, and eigenvector centrality were associated with reduced depressive symptoms, indicating that being well‐integrated within a social network may offer protective benefits. In contrast, high out‐closeness centrality predicted increased depression, suggesting that the effort required to maintain outward‐reaching connections may impose psychological burdens. These findings suggest that targeted interventions should focus on strengthening inward social integration while addressing the stress related to maintaining extensive outward connections, providing direction for more effective youth mental health strategies.

Since multi-attribute control charts have received little attention compared with multivariate variable control charts, this research is concerned with developing a new methodology to employ the multivariate exponentially weighted moving average (MEWMA) charts for m-attribute binomial processes; the attributes being the number of nonconforming items. Moreover, since the variable sample size and sampling interval (VSSI) MEWMA charts detect small process mean shifts faster than the traditional MEWMA, an economic design of the VSSI MEWMA chart is proposed to obtain the optimum design parameters of the chart. The sample size, the sampling interval, and the warning/action limit coefficients are obtained using a genetic algorithm such that the expected total cost per hour is minimized. At the end, a sensitivity analysis has been carried out to investigate the effects of the cost and the model parameters on the solution of the economic design of the VSSI MEWMA chart.

Economic designs (with or without the addition of statistical constraints) provide reasonable approaches to the problem of choosing the parameters of a control chart. For the multivariate exponentially weighted moving average (MEWMA) control chart, the optimal choice of control limit, sample size, exponential weights for past observations and sampling interval according to economic and statistical considerations is a complex problem. This paper provides an algorithm for evaluating the ARL values for the MEWMA chart through simulation and then using these results to determine the optimal chart parameters given process cost and time information.

Quality control charts have proven to be very effective in detecting out of control signals. It is very important to practitioners to determine at what point in the past the signal was initiated. If a control chart signals a change in the process parameter, identifying the time of the change will substantially help the signal diagnostics procedure since it simplifies the search for special causes. In this paper the researchers propose the observations following multivariate normal distribution. They have used multivariate exponentially weighted moving average (MEWMA) control chart to detect signals. This research provides two ways to detect the change point, first MLE, and then neural network is used to identify the time of the change in the parameters (mean) in the past. The researchers intended to assess the performance of two approaches and compare them through computer simulation experiments. The results show that neural network performs effectively and equally well for the whole process dimensions. Thus, the neural network provides process engineers with an accurate and useful estimate of the actual time of the change in the process mean.

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

The progress of website and different social network sites (SNS) have made the clients to communicate on a collective environment. A graph is applied to denote the working of social networks. Graph is nothing but a collection of nodes and edges, where the edges are used to connect the nodes. In social networks, the individuals / entities are represented by nodes and also the edges represent the interaction between entities. The disposition of individuals with connected perceptions, decisions and inclinations is connected with a social networking model, which ends up in the event of virtual clusters or communities. Revealing those groups is useful for frequent claims by discovering a typical analysis space network, discovering the fact of finding a set of similar minded individuals for promoting proposals and to discover macromolecule interface network in biological system. In the literature review, an outsized variety of detecting the community algorithm is proposed and applied to various domains. This research study presents a review of the prevailing algorithms and methodologies used for communities' detection in social networking. Also, this research work tends to conjointly deliberate a number of applications for Community Detection (CD) in social media networks.

Abnormal user detection is one of the key issues in online social network security research. Attackers spread advertising and other malicious messages through stolen accounts, and malicious actions seriously threaten the information security of normal users with the credit system of social networks. For this reason, in the literature, there are a considerable amount of research work which detect abnormal accounts in social networks, however, these efforts ignore the problem of the seamless integration of machine learning with human behaviour-based analysis. This paper reviews the main achievements of abnormal account detection in online social networks in recent years from three aspects: behavioral characteristics, content-based, graph-based, and proposes a new social network abnormal user detection method based on system calls in computer’s kernel. Using enumeration sequence and hidden semi-Markov method, a hierarchical model of anomaly user detection in social networks is established.

Advances in Web 2.0 technologies have led to unstructured information overload in the complex and multidimensional datasets that originate in social networks. This overload can diminish the quality of web-related services such as recommender systems in the social web and can overwhelm users with irrelevant information. Such overload indicates the need to design and develop efficient and accurate information management and retrieval systems in social networks and anticipate recommendations in the semantic context of user interest. Community detection in social networks can help identify higher-order structures that unveil insight into networks and their functional organization and reduce irrelevant information received by the user. In this study, the application of community detection in online social networks is investigated within the framework of topic discovery-based user link identification and is consequently used to uncover implicit user communities (ad hoc communities). In the proposed approach, we use a graph-based information extraction technique that provides for a personalized information retrieval (recommender) system. We hypothesize that the ad hoc communities of users sharing similar interests embedded in a folksonomy-based social network can be identified by overlapping tag clusters in the tag concept hierarchy. Our methodology incorporates the novel information extraction techniques of topic modeling for topic extraction (feature extraction), user profile modeling for user profile extraction, and community extraction from the social graph, modeled in a framework to derive relevant ad hoc user communities of social interest from folksonomy data. Our experimental results demonstrate an accuracy of 70–98 % in community detection using data obtained from CiteULike®.

Autocorrelation leads to a bias estimator of standard control charts. It is important to develop control chart that allows autocorrelation and to evaluate its performance. The objective of this paper is to evaluate the performance of multioutput least square support vector regression (MLS-SVR)-based multivariate exponentially weighted moving average (MEWMA) control chart for monitoring multivariate autocorrelated data. For first order of vector autoregressive (VAR) and first order of vector moving average data, the proposed control chart tends to yield stable in-control average run length at about 200. The proposed control chart becomes more insensitive due to the increase of MEWMA smoothing parameter. In the real application, the proposed method is successfully applied to monitor water turbidity and chlorine residual data in the drinking water manufacturing process.

In order to improve the monitoring efficiency of the multivariable autocorrelation process and reduce the cost of process control, an economic design method of residual Multivariate Exponentially Weighted Moving Average (MEWMA) control charts with variable sample size and sampling intervals (VSSIs) was proposed. First, residual MEWMA control charts with VSSI were designed for monitoring multivariable autocorrelation processes. Second, the economic model of the VSSI residual MEWMA control chart was established, and a data example was given. The optimal design parameters of the control chart were obtained by using a genetic algorithm to minimize the expected cost per unit time. Third, using orthogonal experimental design and regression analysis, a sensitivity analysis of the economic model was carried out to get the relationship between the model parameters and the design parameters of the control chart. Finally, through the optimality analysis, it was verified that the proposed economic model was superior to the models established by the variable sampling intervals strategy and the variable sample size strategy and had less expected cost per unit time.

Community detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social networks.