Pairwise Relationships Research Articles

The mediating role of coping styles between nurses’ workplace bullying and professional quality of life

AimsThis study aimed to explore the relationship between workplace bullying among nurses and their professional quality of life, as well as the mediating role of coping styles between the two factors.BackgroundIn China, the overall status of nurses' professional quality of life is not optimistic, and the problems of low compassion satisfaction and high compassion fatigue persist. Workplace bullying, which is a serious global issue, can negatively impact the mental health and professional quality of nurses. However, it has still not attracted enough attention from managers.MethodsThe study used a cross-sectional research design and surveyed 297 clinical nurses from two tertiary grade A hospitals in Wuhan, China. Data were collected through an online questionnaire survey from March to May 2022. The data were analyzed using descriptive statistical methods, including Pearson correlation analysis and structural equation modeling.ResultsThe score for nurses' workplace bullying was 38.72 ± 12.30. The scores for the three dimensions of professional quality of life were 27.56 ± 4.79 for compassion satisfaction, 30.51 ± 4.33 for burnout, and 28.47 ± 4.65 for secondary trauma stress. The scores for positive coping style and negative coping style were 34.59 ± 5.72 and 20.34 ± 5.08 points, respectively. Workplace bullying had a direct negative effect on compassion satisfaction, as well as positive direct effects on burnout and secondary traumatic stress. Coping styles played a mediating effect between workplace bullying and the pairwise relationships of compassion satisfaction, burnout, and secondary trauma stress.ConclusionWorkplace bullying hurts nurses' professional quality of life while coping styles plays an mediating role between workplace bullying and professional quality of life. Nursing managers can improve nurses' professional quality of life by reducing workplace bullying and enhancing positive coping style.Implications for nursing managementNursing managers can employ management wisdom and techniques to mitigate the presence and detrimental effects of workplace bullying. This, in turn, promotes a positive work environment and enhances the professional quality of life for nurses.

Small groups in multidimensional feature space: Two examples of supervised two-group classification from biomedicine.

Some biomedical datasets contain a small number of samples which have large numbers of features. This can make analysis challenging and prone to errors such as overfitting and misinterpretation. To improve the accuracy and reliability of analysis in such cases, we present a tutorial that demonstrates a mathematical approach for a supervised two-group classification problem using two medical datasets. A tutorial provides insights on effectively addressing uncertainties and handling missing values without the need for removing or inputting additional data. We describe a method that considers the size and shape of feature distributions, as well as the pairwise relations between measured features as separate derived features and prognostic factors. Additionally, we explain how to perform similarity calculations that account for the variation in feature values within groups and inaccuracies in individual value measurements. By following these steps, a more accurate and reliable analysis can be achieved when working with biomedical datasets that have a small sample size and multiple features.

Demographic and travel characteristics and self-reported predeparture SARS-CoV-2 testing behavior in air passengers entering the United States from foreign destinations from July to September 2021.

From January 2021 to June 2022, the United States Centers for Disease Control and Prevention required predeparture SARS-CoV-2 testing for all air passengers arriving into the United States from a foreign country. Using data collected during a surveillance project, we described predeparture testing behavior among a convenience sample of international air passengers entering the United States from July to September 2021 at six US ports of entry. We analyzed pairwise relationships between self-reported test type, test timing, demographic and travel characteristics, and COVID-19 vaccination status using chi-square and Fisher's exact tests. Participants were more likely to get a NAAT versus antigen test if they identified as non-Hispanic Asian or Pacific Islander (68.2%, n = 173), non-Hispanic Black (62.6%, n = 147), or if they preferred not to report race and ethnicity (60.8%, n = 209) when compared to those who identified as non-Hispanic White (47.1%, n = 1086, all p < 0.05). Those who identified as Hispanic or Latino (n = 671) were less likely to get a NAAT than the non-Hispanic White group (39.5% vs. 47.1%, p < 0.05). Participants arriving in the US from the Americas were less likely to get a NAAT (38.5%, n = 871) compared to those arriving from Europe (45.5%, n = 1165, p < 0.05). Participants who reported receiving their predeparture test 2 days or 3 or more days before departure were more likely to report receiving a NAAT (52.2%, n = 879, and 60.2%, n = 410, respectively) than those who reported testing within 1 day (41.4%, n = 1040, all p < 0.001) of departure. Test type was significantly associated with race and ethnicity, departure region, and test timing. Differences likely reflected regional disparities in the availability of tests at the time of the activity. Discrepancies in predeparture test timing and type worldwide may have consequences for the effectiveness and equity of travel requirements in future pandemics.

A New Approach on Bipolar Second Degree Perfect Fuzzy Matching for Bipolar Fuzzy Graphs Based on Vertices

Graph is a visual representation of data. In mathematics, graph is a structure which creates the pairwise relation between objects. The objects are known as nodes or vertices and the connection between two nodes are known as links or edges. A graph is a symmetric binary relation on a non-empty set whereas Fuzzy graph is a symmetric binary fuzzy relation on a fuzzy subset. Bipolar Fuzzy graph is a generalization of graph theory by using bipolar Fuzzy set. The bipolar Fuzzy sets are the extension of Fuzzy sets. Our main objective is to introduce Bipolar Second Degree Perfect Fuzzy Matching for Bipolar Fuzzy Graph based on vertices. Secondly, we proved the necessary conditions under which they are equivalent and also proved that for a particular condition a perfect second-degree fuzzy matching is not a regular bipolar fuzzy graph. Next, we discussed about perfect second-degree Fuzzy matching and second degree fuzzy matching number for bipolar fuzzy graph on the cycle.

A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation

Human activity intensity prediction, i.e., estimating the dynamic population distribution, is crucial to many location-based applications, particularly intelligent transportation systems and urban planning. Geographical artificial intelligence (GeoAI) is an emerging field that has contributed many new modeling approaches for spatiotemporal phenomena, especially spatiotemporal graph neural networks (ST-GNNs). However, there are two challenges to the prediction of human activity intensity. On the one hand, accurate predictions require modeling not only pairwise spatial relationships, e.g., distance and spatial flow but also complex high-order relationships, such as clusters and groups. On the other hand, it is not appropriate to incorporate prior knowledge on the spatial structure of data with subjective assumptions using graphs with predefined topologies or weights. To mitigate such challenges, a novel deep learning-based model was proposed, namely, the Hypergraph-based Hybrid Graph Convolutional Network (HyGCN). In our study, high-order relationships are modeled by hypergraph convolutions, and pairwise relationships are captured in graph convolutions. A spatial fusion layer is added to fuse the pairwise and high-order relationships. Then, adaptive graph and hypergraph generation methods are proposed to interpret edge weights and understand the learned geographic relationships in accordance with principles. After systematic experiments on large-scale mobility data, our results demonstrate that HyGCN outperforms state-of-the-art models. The learned geospatial knowledge can be disentangled and explained from the perspectives of geographic principles. This study offers new insights for not only revisiting geographic laws in GeoAI but also utilizing spatiotemporal knowledge in deep learning models.

CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation

In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. First, most existing SBR approaches utilize limited information from short user–item interaction sequences and suffer from the data sparsity problem of session data. Second, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate sessionwise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better tradeoff between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.

Exploiting Conversation-Branch-Tweet HyperGraph Structure to Detect Misinformation on Social Media

The spread of misinformation on social media is a serious issue that can have negative consequences for public health and political stability. While detecting and identifying misinformation can be challenging, many attempts have been made to address this problem. However, traditional models that focus on pairwise relationships on misinformation propagation paths may not be effective in capturing the underlying connections among multiple tweets. To address this limitation, the proposed “Conversation-Branch-Tweet” hypergraph convolutional network (CBT-HGCN) uses a hypergraph to represent the internal structure and content of tweet data, with tweets and their replies viewed as nodes and hyperedges, respectively. The model first pre-processes the tweets of a conversation and then uses a pre-trained model as an encoder to extract node information. Finally, a hypergraph convolution network is used as an information fuser for classification. Experimental results on three benchmark datasets (Twitter15, Twitter16, and Pheme) show that the proposed model outperforms several strong baseline models and achieves state-of-the-art performance. This indicates that the CBT-HGCN approach is effective in detecting and identifying misinformation on social media by capturing the underlying connections among multiple tweets.

A Multisource Data Fusion-based Heterogeneous Graph Attention Network for Competitor Prediction

Competitor identification is an essential component of corporate strategy. With the rapid development of artificial intelligence, various data-mining methodologies and frameworks have emerged to identify competitors. In general, the competitiveness among companies is determined by both market commonality and resource similarity. However, because resource information is more difficult to obtain than market information, existing studies primarily identify competitors via market commonality. To address this limitation, we introduce multisource company descriptions as well as heterogeneous business relationships, and we propose a novel method for simultaneously mining the market commonality and resource similarity. First, we use multisource company descriptions to represent companies and transform the heterogeneous business relationships into a heterogeneous business network. Then, we propose a novel multisource data fusion-based heterogeneous graph attention network (MHGAT) to learn the pairwise competitive relationships between companies. Specifically, a graph neural network-based model is proposed to learn the embeddings of companies by preserving their competition, and a multilevel attention framework is designed to integrate the embeddings from neighboring company level, heterogeneous relationship level, and multisource description level. Finally, experiments on a real-world dataset verify the effectiveness of our proposed MHGAT and demonstrate the usefulness of company descriptions and business relationships in competitor identification.

View-coherent correlation consistency for semi-supervised semantic segmentation

Semi-supervised semantic segmentation needs rich and robust supervision for unlabeled data. However, promoting or punishing feature similarities with vanilla contrastive learning can be unreliable for semi-supervised semantic segmentation: pixel pairs are assigned as either positive or negative based on noisy pseudo labels, and both reliable and wrongly-assigned pairs receive uniform penalties. To address this issue, we propose correlation consistency learning, which leverages rich pairwise relationships in self-correlation matrices and matches them to the similarities between soft pseudo labels to provide robust supervision. Unlike vanilla contrastive learning, our approach prioritizes pairs with highly confident pseudo labels and applies weaker penalties for pairs that are less confident. We also introduce a strong semi-supervised learning pipeline that applies data augmentation in a view-coherent manner: even under complex augmentation strategies, for each pixel, a match can be found in different augmentation views. The novelties of the proposed method are the correlation consistency loss and the view-coherent data augmentation, and their combination gives us the view-coherent correlation consistency (VC3) system, which achieves state-of-the-art results in several semi-supervised settings on two datasets.

Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation

Session-based recommendation (SBR) systems aim to utilize the user’s short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at https://github.com/SUSTechBruce/RESTC-Source-code .

The GNAR-edge model: a network autoregressive model for networks with time-varying edge weights

Abstract In economic and financial applications, there is often the need for analysing multivariate time series, comprising of time series for a range of quantities. In some applications, such complex systems can be associated with some underlying network describing pairwise relationships among the quantities. Accounting for the underlying network structure for the analysis of this type of multivariate time series is required for assessing estimation error and can be particularly informative for forecasting. Our work is motivated by a dataset consisting of time series of industry-to-industry transactions. In this example, pairwise relationships between Standard Industrial Classification (SIC) codes can be represented using a network, with SIC codes as nodes and pairwise transactions between SIC codes as edges, while the observed time series of the amounts of the transactions for each pair of SIC codes can be regarded as time-varying weights on the edges. Inspired by Knight et al. (2020, J. Stat. Softw., 96, 1–36), we introduce the GNAR-edge model which allows modelling of multiple time series utilizing the network structure, assuming that each edge weight depends not only on its past values, but also on past values of its neighbouring edges, for a range of neighbourhood stages. The method is validated through simulations. Results from the implementation of the GNAR-edge model on the real industry-to-industry data show good fitting and predictive performance of the model. The predictive performance is improved when sparsifying the network using a lead–lag analysis and thresholding edges according to a lead–lag score.

Explainable AI-Based Identification of Contributing Factors to the Mood State Change in Children and Adolescents with Pre-Existing Psychiatric Disorders in the Context of COVID-19-Related Lockdowns in Greece

The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards the impact of the elongation of COVID-19-related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies focus on individuals, such as students, adults, and youths, among others, with little attention being given to the elongation of COVID-19-related measures and their impact on a special group of individuals, such as children and adolescents with diagnosed developmental and psychiatric disorders. In addition, most of these studies adopt statistical methodologies to identify pair-wise relationships among factors, an approach that limits the ability to understand and interpret the impact of various factors. In response, this study aims to adopt an explainable machine learning approach to identify factors that explain the deterioration or amelioration of mood state in a youth clinical sample. The purpose of this study is to identify and interpret the impact of the greatest contributing features of mood state changes on the prediction output via an explainable machine learning pipeline. Among all the machine learning classifiers, the Random Forest model achieved the highest effectiveness, with 76% best AUC-ROC Score and 13 features. The explainability analysis showed that stress or positive changes derived from the imposing restrictions and COVID-19 pandemic are the top two factors that could affect mood state.

Implications of methodologies for integrating empirical kinships into ex situ population management using PMx: A case study of Baer's Pochard (Aythya baeri) in North America.

The application of molecular tools to population management can improve the long-term genetic viability of ex situ populations. In this study, we aimed to understand the implications of integrating empirical kinships into the genetic management of an ex situ population of the endangered waterfowl, Baer's pochard (Aythya baeri), in North America. Single nucleotide polymorphism data were generated for 141 Baer's pochard using double digest restriction site-associated DNA sequencing and empirical kinships were derived and integrated into the population management software PMx. Analyses suggested 37.7% of pairwise relationships previously assumed to be unrelated were first, second, or third-order relatives. We determined that most genetic summary statistics were impacted through the calculation of the population's mean kinship, which increased from MK¯=0.0772 to MK¯=0.2074 after empirical kinships were integrated into our analyses. Our results also revealed the importance of understanding how molecular kinships derived from a particular estimator are scaled, if the scale differs significantly from pedigree-based kinships. We describe the theory behind the genetic metrics impacted and provide general guidance on incorporating empirical kinships into ex situ population management as well as provide suggestions for sampling strategies to minimize the biases inherent in merging two types of kinship estimators.

Enhancing Enterprise Credit Risk Assessment with Cascaded Multi-level Graph Representation Learning

The assessment of Enterprise Credit Risk (ECR) is a critical technique for investment decisions and financial regulation. Previous methods usually construct enterprise representations by credit-related indicators, such as liquidity and staff quality. However, indicators of many enterprises are not accessible, especially for the small- and medium-sized enterprises. To alleviate the indicator deficiency, graph learning based methods are proposed to enhance enterprise representation learning by the neighbor structure of enterprise graphs. However, existing methods usually only focus on pairwise relationships, and overlook the ubiquitous high-order relationships among enterprises, e.g., supply chain connecting multiple enterprises. To resolve this issue, we propose a Multi-Structure Cascaded Graph Neural Network framework (MS-CGNN) for ECR assessment. It enhances enterprise representation learning based on enterprise graph structures of different granularity, including knowledge graphs of pairwise relationships, homogeneous and heterogeneous hypergraphs of high-order relationships. To distinguish influences of different types of hyperedges, MS-CGNN redefine new type-dependent hyperedge weight matrices for heterogeneous hypergraph convolutions. Experimental results show that MS-CGNN achieves state-of-the-art performance on real-world ECR datasets.

Comprehensive learning TLBO with recursive precedence-based solution construction and multilevel local search for the linear ordering problem

The linear ordering problem (LOP) is a difficult permutation-based optimization task with a multitude of practical applications in different areas. The objective of the LOP is to maximize the total of the benefits associated with the pairwise precedence relations between the permuted items. Since it is NP-hard, several approximate methods have been developed to quickly produce acceptable solutions. We aim to design a specialized teaching–learning-based optimization (TLBO) method that exploits the permutation structure of the LOP. TLBO is a modern population-based metaheuristic that mimics the conventional process of transmitting knowledge and skills in a classroom. Here we extend and improve TLBO by employing a comprehensive learning strategy. In this strategy, a selected group of learners from the current class are employed as exemplars to generate new solutions. The key component of our comprehensive learning TLBO (CL-TLBO) is a recursive permutation construction procedure. It is an effective pivot-based partitioning algorithm that focuses on the pairwise precedences between permutation items in exemplar solutions to generate new candidate solutions. For better performance, we investigate different ways of selecting the pivot item at each partitioning step. Furthermore, we offer a multilevel local search technique to refine the solutions of sub-problems that are constructed during the recursive partitioning process. The experimental evaluation demonstrates a very competitive performance of the developed CL-TLBO method when compared with various well-established techniques.

Dynamic hypergraph convolutional network for multimodal sentiment analysis

Multimodal sentiment analysis (MSA) aims to detect the sentiments from language (text), audio, and visual (facial expressions) modalities. The main challenge in MSA is how to efficiently model intra-modality and inter-modality dynamics. With the advent of graph convolution network (GCN), graph-based models are proposed to solve the challenge. However, general graphs contain only two nodes per edge, which limits the exploitation of high-order interactions. Moreover, current graph-based models mainly aggregate the features of each node during fusion, while the features of connected edges are not well mined. In this paper, we introduce dynamic hypergraph convolution networks to MSA for the first time and propose a Multimodal Dynamic Hypergraph Network (MDH) to learn intra- and inter-modality dynamics. Hypergraphs provide a natural approach to capture transcendental pairwise relations, and their potential for MSA remains unexplored. MDH mainly consists of three components: Unimodal Encoder, Dynamic Hypergraph Enhancement Network (DHEN), and HyperFusion module. Specifically, DHEN is composed of Cross-modal Affine, Hypergraph Construction, and Hypergraph Aggregation modules. As for the intra-modality dynamics, MDH utilizes Hypergraph Construction and Aggregation modules to model the interactions within time steps for each modality. As for the inter-modality dynamics, MDH implements Cross-modal Affine and HyperFusion modules to learn the relationships of the modalities. In addition, multi-task learning has been implemented to optimize the learning process for multimodal tasks. Experiments show that MDH outperforms graph-based models on CMU-MOSI and CMU-MOSEI datasets, as well as obtains new state-of-the-art results on CH-SIMS dataset. Furthermore, we conduct external experiments to explore the effectiveness of MDH and the effect of model depth with different graph networks.

Nonassortative relationships between groups of nodes are typical in complex networks.

Decomposing a graph into groups of nodes that share similar connectivity properties is essential to understand the organization and function of complex networks. Previous works have focused on groups with specific relationships between group members, such as assortative communities or core-periphery structures, developing computational methods to find these mesoscale structures within a network. Here, we go beyond these two traditional cases and introduce a methodology that is able to identify and systematically classify all possible community types in directed multi graphs, based on the pairwise relationship between groups. We apply our approach to 53 different networks and find that assortative communities are the most common structures, but that previously unexplored types appear in almost every network. A particularly prevalent new type of relationship, which we call a source-basin structure, has information flowing from a sparsely connected group of nodes (source) to a densely connected group (basin). We look in detail at two online social networks-a new network of Twitter users and a well-studied network of political blogs-and find that source-basin structures play an important role in both of them. This confirms not only the widespread appearance of nonassortative structures but also the potential of hitherto unidentified relationships to explain the organization of complex networks.

Local Embedding Learning via Landmark-Based Dynamic Connections.

Linear discriminant analysis (LDA) is one of the most effective and popular methods to reduce the dimensionality of data with Gaussian assumption. However, LDA cannot handle non-Gaussian data because the center point is incompetent to represent the distribution of data. Some existing methods based on graph embedding focus on exploring local structures via pairwise relationships of data for addressing the non-Gaussian issue. Due to massive pairwise relationships, the computational complexity is high as well as the locally optimal solution is hard to find. To address these issues, we propose a novel and efficient local embedding learning via landmark-based dynamic connections (LDC) in which we leverage several landmarks to represent different subclusters in the same class and establish the connections between each point and landmark. Furthermore, in order to explore the relationship of landmarks pairwise more precisely, the relationship between each point and their corresponding neighbor landmarks are found in the optimal subspace, rather than the original space, which can avoid the negative influence of the noises. We also propose an efficient iterative algorithm to deal with the proposed ratio minimization problem. Extensive experiments conducted on several real-world datasets have demonstrated the advantages of the proposed method.

MG-ASTN: Multigraph Framework With Attentive Spatial–Temporal Networks for Crowd Mobility Prediction

Predicting urban crowd patterns/flows is a challenging task due to complex spatial-temporal (ST) dependencies. In this paper, we aim to examine and report the capability and effectiveness of the current most widely used Graph Convolutional Networks (GCNs) on mobile data analysis in ST networks for crowd flow forecasting. Specifically, we propose a novel dual-stream framework leveraging Multi-Graph with Attentive Spatial-Temporal Networks (MG-ASTN) to simultaneously predict crowd in-out flow and OD flow based on the trajectory data collected by on-board devices (e.g., GPS). MG-ASTN utilizes Multi-Graph Convolutional Networks encoding non-Euclidean correlations to explore pair-wise relationships among regions. In addition, we further apply a cross-channel attention mechanism with 3D Temporal Convolutional Network to address the heterogeneity of ST features and capture more meaningful data representations for multi-task learning. In the evaluation, we conduct experiments based on 2 real-world datasets and verify most well-known state-of-the-art methods for crowd flow prediction. The results demonstrate that MG-ASTN could outperform other solutions -in-out flow prediction with lowest RMSE and MAE, and OD flow prediction beyond others in most cases, thus it has great potential in modeling the complex correlations among regions in ST networks and enabling accurate prediction in urban computing.

Self-Supervised Hypergraph Representation Learning for Sociological Analysis

Modern sociology has profoundly uncovered many convincing social criteria for behavioral analysis. Unfortunately, many of them are too subjective to be measured and very challenging to be presented in online social networks (OSNs) for the large data volume and complicated environments to be explored. On the other hand, data mining techniques can better find data patterns but many of them leave behind unnatural understanding to humans. Although there are some works trying to integrate social observations for specific tasks, they are still hard to be applied to more general cases. In this paper, we propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria. Our highlights are three-fold: First, we propose an effective hypergraph awareness and a fast line graph construction framework. The hypergraph can more profoundly indicate the interactions between individuals and their environments because each edge in the hypergraph (a.k.a hyperedge) contains more than two nodes, which is perfect to describe social. A line graph treats each social environment as a super node with the underlying influence between different environments. In this way, we go beyond traditional pair-wise relations and explore richer patterns under various sociological criteria; Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users, users to environments, environment to users, and environments to environments. The neural network can be learned via a task-free method, making our model very flexible to support various data mining tasks and sociological analysis; Third, we propose both qualitative and quantitive solutions to effectively evaluate the most common sociological criteria like social conformity, social equivalence, environmental evolving and social polarization. Our extensive experiments show that our framework can better support both data mining tasks for online user behaviors and sociological analysis.