This article proposes a machine learning (ML)-based approach to solve a graph optimization problem, named network intervention with limited degradation (NILD), which aims at adding new edges to augment the graph to minimize the local clustering coefficient (LCC) of a target node. The main application of NILD is to perform network intervention , to improve the mental well-being of individuals. This article proposes a new framework, named network intervention with self-supervision (NISS), which employs reinforcement learning and self-supervised learning (SSL) to effectively solve the problem. We propose two new effective pretext tasks in SSL, Distance-to-target prediction task and LCC increment prediction task to improve the model performance. In addition, we also propose two new embedding approaches, neighborhood embedding (NE) and constraint property embedding (CPE), to capture the structural information of the graph. Extensive experiments on multiple real social networks and synthetic datasets show that our proposed approach significantly outperforms the other state-of-the-art baselines, including ML-based baselines and deterministic algorithms.
本文提出了一种基于机器学习（ML）的方法来解决图优化问题，并将其命名为 "有限退化的网络干预（NILD）"，其目的是添加新的边以增强图，从而使目标节点的局部聚类系数（LCC）最小化。NILD 的主要应用是进行网络干预，以改善个人的心理健康。本文提出了一个新的框架，名为 "自我监督网络干预（NISS）"，它采用强化学习和自我监督学习（SSL）来有效解决这一问题。我们在 SSL 中提出了两个新的有效借口任务，即目标距离预测任务和 LCC 增量预测任务，以提高模型性能。此外，我们还提出了两种新的嵌入方法：邻域嵌入（NE）和约束属性嵌入（CPE），以捕捉图的结构信息。在多个真实社交网络和合成数据集上的广泛实验表明，我们提出的方法明显优于其他最先进的基线方法，包括基于 ML 的基线方法和确定性算法。
{"title":"Learning to Augment Graphs: Machine-Learning-Based Social Network Intervention With Self-Supervision","authors":"Chih-Chieh Chang;Chia-Hsun Lu;Ming-Yi Chang;Chao-En Shen;Ya-Chi Ho;Chih-Ya Shen","doi":"10.1109/TCSS.2023.3340230","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3340230","url":null,"abstract":"This article proposes a machine learning (ML)-based approach to solve a graph optimization problem, named network intervention with limited degradation (NILD), which aims at adding new edges to augment the graph to minimize the local clustering coefficient (LCC) of a target node. The main application of NILD is to perform \u0000<italic>network intervention</i>\u0000, to improve the mental well-being of individuals. This article proposes a new framework, named network intervention with self-supervision (NISS), which employs reinforcement learning and self-supervised learning (SSL) to effectively solve the problem. We propose two new effective pretext tasks in SSL, \u0000<italic>Distance-to-target</i>\u0000 prediction task and \u0000<italic>LCC increment</i>\u0000 prediction task to improve the model performance. In addition, we also propose two new embedding approaches, neighborhood embedding (NE) and constraint property embedding (CPE), to capture the structural information of the graph. Extensive experiments on multiple real social networks and synthetic datasets show that our proposed approach significantly outperforms the other state-of-the-art baselines, including ML-based baselines and deterministic algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/TCSS.2024.3350675
Muhammad Nadeem;Muhammad Irfan Malik;Arif Ullah;Novaira Junaid
Smoking tobacco is not only a public health issue but also has severe economic connotations too. Due to substantial economic and health effects of tobacco pandemic, it is now an urgent and obvious public health priority worldwide. Increased rate of its usage in Pakistan has negatively impacted people's health, families, and society. In order to cure issue, there is need to find factors responsible for pervasiveness of tobacco use in Pakistan. Thus, study will explore determinants of tobacco smoking in Pakistan. Study uses Pakistan demographic and health survey data. Analysis comprises of theoretical reasoning, association tests, and logistic regression. In analysis, tobacco use has been used as dependent variable, while age, occupation, region, place of residence, household wealth status, and education level have been used as independent variables. Results indicate that odds of any kind of tobacco use are highest for Punjab, it is more prevalent in urban areas, highest odds are for people 40 years of age and above, lowest odds are for people with higher education level, highest odds are for people either self-employed or engaged in agricultural activities, and it is more prevalent in poor households. Based upon results of study, it is conferred that in order to control tobacco use targeted interventions are needed and there is need to focus on: urban areas, less educated people, poor households, people of age 40 years and above, and people who are self-employed or engaged in agricultural activities.
{"title":"Smoking Dynamics: Factors Supplementing Tobacco Smoking in Pakistan","authors":"Muhammad Nadeem;Muhammad Irfan Malik;Arif Ullah;Novaira Junaid","doi":"10.1109/TCSS.2024.3350675","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3350675","url":null,"abstract":"Smoking tobacco is not only a public health issue but also has severe economic connotations too. Due to substantial economic and health effects of tobacco pandemic, it is now an urgent and obvious public health priority worldwide. Increased rate of its usage in Pakistan has negatively impacted people's health, families, and society. In order to cure issue, there is need to find factors responsible for pervasiveness of tobacco use in Pakistan. Thus, study will explore determinants of tobacco smoking in Pakistan. Study uses Pakistan demographic and health survey data. Analysis comprises of theoretical reasoning, association tests, and logistic regression. In analysis, tobacco use has been used as dependent variable, while age, occupation, region, place of residence, household wealth status, and education level have been used as independent variables. Results indicate that odds of any kind of tobacco use are highest for Punjab, it is more prevalent in urban areas, highest odds are for people 40 years of age and above, lowest odds are for people with higher education level, highest odds are for people either self-employed or engaged in agricultural activities, and it is more prevalent in poor households. Based upon results of study, it is conferred that in order to control tobacco use targeted interventions are needed and there is need to focus on: urban areas, less educated people, poor households, people of age 40 years and above, and people who are self-employed or engaged in agricultural activities.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1109/TCSS.2023.3349071
Hexiang Huang;Xuan Zhang;Jishu Wang;Chen Gao;Xue Li;Rui Zhu;Qiuying Ma
Recent years, the successful application of block-chain in cryptocurrency has attracted a lot of attention, but it has also led to a rapid growth of illegal and criminal activities. Phishing scams have become the most serious type of crime in Ethereum. Some existing methods for phishing scams detection have limitations, such as high complexity, poor scalability, and high latency. In this article, we propose a novel framework named phishing detection on Ethereum via augmentation ego-graph based on graph neural network (PEAE-GNN). First, we obtain account labels and transaction records from authoritative websites and extract ego-graphs centered on labeled accounts. Then we propose a feature augmentation strategy based on structure features, transaction features and interaction intensity to augment the node features, so that these features of each ego-graph can be learned. Finally, we present a new graph-level representation, sorting the updated node features in descending order and then taking the mean value of the top n to obtain the graph representation, which can retain key information and reduce the introduction of noise. Extensive experimental results show that PEAE-GNN achieves the best performance on phishing detection tasks. At the same time, our framework has the advantages of lower complexity, better scalability, and higher efficiency, which detects phishing accounts at early stage.
近年来，区块链在加密货币中的成功应用吸引了大量关注，但也导致违法犯罪活动迅速增长。网络钓鱼诈骗已成为以太坊中最严重的犯罪类型。现有的一些钓鱼欺诈检测方法存在局限性，如复杂性高、可扩展性差、延迟高。在本文中，我们提出了一种名为 "通过基于图神经网络的增强自我图（PEAE-GNN）检测以太坊上的网络钓鱼 "的新型框架。首先，我们从权威网站获取账户标签和交易记录，并提取以标签账户为中心的自我图。然后，我们提出了一种基于结构特征、交易特征和交互强度的特征增强策略，以增强节点特征，从而学习每个自我图的这些特征。最后，我们提出了一种新的图层表示法，将更新后的节点特征按降序排序，然后取前 n 个节点特征的平均值来获得图层表示法，这样既能保留关键信息，又能减少噪声的引入。大量实验结果表明，PEAE-GNN 在网络钓鱼检测任务中取得了最佳性能。同时，我们的框架具有更低的复杂度、更好的可扩展性和更高的效率，能在早期阶段检测到钓鱼账户。
{"title":"PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural Network","authors":"Hexiang Huang;Xuan Zhang;Jishu Wang;Chen Gao;Xue Li;Rui Zhu;Qiuying Ma","doi":"10.1109/TCSS.2023.3349071","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3349071","url":null,"abstract":"Recent years, the successful application of block-chain in cryptocurrency has attracted a lot of attention, but it has also led to a rapid growth of illegal and criminal activities. Phishing scams have become the most serious type of crime in Ethereum. Some existing methods for phishing scams detection have limitations, such as high complexity, poor scalability, and high latency. In this article, we propose a novel framework named phishing detection on Ethereum via augmentation ego-graph based on graph neural network (PEAE-GNN). First, we obtain account labels and transaction records from authoritative websites and extract ego-graphs centered on labeled accounts. Then we propose a feature augmentation strategy based on structure features, transaction features and interaction intensity to augment the node features, so that these features of each ego-graph can be learned. Finally, we present a new graph-level representation, sorting the updated node features in descending order and then taking the mean value of the top n to obtain the graph representation, which can retain key information and reduce the introduction of noise. Extensive experimental results show that PEAE-GNN achieves the best performance on phishing detection tasks. At the same time, our framework has the advantages of lower complexity, better scalability, and higher efficiency, which detects phishing accounts at early stage.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1109/TCSS.2023.3335162
Can Qiu;Dengxiu Yu;Zhen Wang;C. L. Philip Chen
This article proposes a teacher-guided peer learning approach that employs a continuous action iterated dilemma (CAID) model based on an incremental network. Traditional peer learning approaches often assume static communication relationships between students, which is not consistent with actual society, and this affects the effectiveness of peer learning. Additionally, every student is a highly unique individual, and using a single mathematical model to mimic their behavior would result in research findings with limited applicability. Therefore, this article presents several innovations. First, we propose an incremental network generation algorithm that generates an effective communication network to improve classroom efficiency by enhancing the convergence of information between classmates. Second, considering the multiple unknown nonlinear environmental impacts, we design a student dynamic model based on CAID with multiple layers of nonlinearity to fit the different environmental impacts that different students receive. Finally, based on the incremental network and student dynamic model, we design the Lyapunov function to prove the convergence of the proposed model. This mathematical proof ensures that the proposed model is stable and unaffected by parameters, making it more applicable.
{"title":"Teacher-Guided Peer Learning With Continuous Action Iterated Dilemma Based on Incremental Network","authors":"Can Qiu;Dengxiu Yu;Zhen Wang;C. L. Philip Chen","doi":"10.1109/TCSS.2023.3335162","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3335162","url":null,"abstract":"This article proposes a teacher-guided peer learning approach that employs a continuous action iterated dilemma (CAID) model based on an incremental network. Traditional peer learning approaches often assume static communication relationships between students, which is not consistent with actual society, and this affects the effectiveness of peer learning. Additionally, every student is a highly unique individual, and using a single mathematical model to mimic their behavior would result in research findings with limited applicability. Therefore, this article presents several innovations. First, we propose an incremental network generation algorithm that generates an effective communication network to improve classroom efficiency by enhancing the convergence of information between classmates. Second, considering the multiple unknown nonlinear environmental impacts, we design a student dynamic model based on CAID with multiple layers of nonlinearity to fit the different environmental impacts that different students receive. Finally, based on the incremental network and student dynamic model, we design the Lyapunov function to prove the convergence of the proposed model. This mathematical proof ensures that the proposed model is stable and unaffected by parameters, making it more applicable.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-16DOI: 10.1109/TCSS.2023.3334823
Jordan Vice;Masood Mehmood Khan;Tele Tan;Iain Murray;Svetlana Yanushkevich
Macrolevel facial muscle variations, as used for building models of seven discrete facial expressions, suffice when distinguishing between macrolevel human affective states but won’t discretise continuous and dynamic microlevel variations in facial expressions. We present a hierarchical separation and classification network (HSCN) for discovering dynamic, continuous, and macro- and microlevel variations in facial expressions of affective states. In the HSCN, we first invoke an unsupervised cosine similarity-based separation method on continuous facial expression data to extract twenty-one dynamic facial expression classes from the seven common discrete affective states. The between-clusters separation is then optimized for discovering the macrolevel changes resulting from facial muscle activations. A following step in the HSCN separates the upper and lower facial regions for realizing changes pertaining to upper and lower facial muscle activations. Data from the two separated facial regions are then clustered in a linear discriminant space using similarities in muscular activation patterns. Next, the actual dynamic expression data are mapped onto discriminant features for developing a rule-based expert system that facilitates classifying twenty-one upper and twenty-one lower microexpressions. Invoking the random forest algorithm would classify twenty-one macrolevel facial expressions with 76.11% accuracy. A support vector machine (SVM), used separately on upper and lower facial regions in tandem, could classify them with respective accuracies of 73.63% and 87.68%. This work demonstrates a novel and effective method of dynamic assessment of affective states. The HSCN further demonstrates that facial muscle variations gathered from either upper, lower, or full-face would suffice classifying affective states. We also provide new insight into discovery of microlevel facial muscle variations and their utilization in dynamic assessment of facial expressions of affective states.
{"title":"A Hierarchical Separation and Classification Network for Dynamic Microexpression Classification","authors":"Jordan Vice;Masood Mehmood Khan;Tele Tan;Iain Murray;Svetlana Yanushkevich","doi":"10.1109/TCSS.2023.3334823","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3334823","url":null,"abstract":"Macrolevel facial muscle variations, as used for building models of seven discrete facial expressions, suffice when distinguishing between macrolevel human affective states but won’t discretise continuous and dynamic microlevel variations in facial expressions. We present a hierarchical separation and classification network (HSCN) for discovering dynamic, continuous, and macro- and microlevel variations in facial expressions of affective states. In the HSCN, we first invoke an unsupervised cosine similarity-based separation method on continuous facial expression data to extract twenty-one dynamic facial expression classes from the seven common discrete affective states. The between-clusters separation is then optimized for discovering the macrolevel changes resulting from facial muscle activations. A following step in the HSCN separates the upper and lower facial regions for realizing changes pertaining to upper and lower facial muscle activations. Data from the two separated facial regions are then clustered in a linear discriminant space using similarities in muscular activation patterns. Next, the actual dynamic expression data are mapped onto discriminant features for developing a rule-based expert system that facilitates classifying twenty-one upper and twenty-one lower microexpressions. Invoking the random forest algorithm would classify twenty-one macrolevel facial expressions with 76.11% accuracy. A support vector machine (SVM), used separately on upper and lower facial regions in tandem, could classify them with respective accuracies of 73.63% and 87.68%. This work demonstrates a novel and effective method of dynamic assessment of affective states. The HSCN further demonstrates that facial muscle variations gathered from either upper, lower, or full-face would suffice classifying affective states. We also provide new insight into discovery of microlevel facial muscle variations and their utilization in dynamic assessment of facial expressions of affective states.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Core–periphery structure is an essential mesoscale feature in complex networks. Previous researches mostly focus on discriminative approaches, while in this work we propose a generative model called masked Bayesian nonnegative matrix factorization. We build the model using two pair affiliation matrices to indicate core–periphery pair associations and using a mask matrix to highlight connections to core nodes. We propose an approach to infer the model parameters and prove the convergence of variables with our approach. Besides the abilities as traditional approaches, it is able to identify core scores with overlapping core–periphery pairs. We verify the effectiveness of our method using randomly generated networks and real-world networks. Experimental results demonstrate that the proposed method outperforms traditional approaches.
{"title":"Core–Periphery Detection Based on Masked Bayesian Nonnegative Matrix Factorization","authors":"Zhonghao Wang;Ru Yuan;Jiaye Fu;Ka-Chun Wong;Chengbin Peng","doi":"10.1109/TCSS.2023.3347406","DOIUrl":"10.1109/TCSS.2023.3347406","url":null,"abstract":"Core–periphery structure is an essential mesoscale feature in complex networks. Previous researches mostly focus on discriminative approaches, while in this work we propose a generative model called masked Bayesian nonnegative matrix factorization. We build the model using two pair affiliation matrices to indicate core–periphery pair associations and using a mask matrix to highlight connections to core nodes. We propose an approach to infer the model parameters and prove the convergence of variables with our approach. Besides the abilities as traditional approaches, it is able to identify core scores with overlapping core–periphery pairs. We verify the effectiveness of our method using randomly generated networks and real-world networks. Experimental results demonstrate that the proposed method outperforms traditional approaches.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The action of spreading false information through fake news articles presents a significant danger to society because it has the ability to shape public opinion with inaccurate facts. This can lead to negative effects, such as reduced trust in institutions and the promotion of conflict, division, and even violence. In this article, a text augmentation technique is introduced as a means of generating new data from preexisting fake news datasets. This approach has the potential to enhance classifier performance by a range of 3%–11%. It can also be utilized to launch a successful attack on trained classifiers, with up to a 90% success rate. However, the success rate of these attacks decreased to less than 28% when the model was retrained with the generated adversarial examples. These results demonstrate the effectiveness of text augmentation as a viable method for detecting fake news and increasing classifier accuracy and performance, as well as its ability to be utilized to perform adversarial machine learning (ML) and improve the resilience of ML algorithms.
通过假新闻文章传播虚假信息的行为对社会造成了极大的危害，因为它有能力用不准确的事实塑造公众舆论。这可能会导致负面影响，如降低对机构的信任，助长冲突、分裂甚至暴力。本文介绍了一种文本增强技术，作为从已有的假新闻数据集中生成新数据的一种手段。这种方法有可能将分类器的性能提高 3% 到 11%。它还可以用来对训练有素的分类器发起成功攻击，成功率高达 90%。然而，当使用生成的对抗示例对模型进行重新训练时，这些攻击的成功率降低到了 28% 以下。这些结果证明了文本增强作为检测假新闻、提高分类器准确性和性能的一种可行方法的有效性，以及利用它进行对抗式机器学习（ML）和提高 ML 算法复原力的能力。
{"title":"Effect of Text Augmentation and Adversarial Training on Fake News Detection","authors":"Hadeer Ahmed;Issa Traore;Sherif Saad;Mohammad Mamun","doi":"10.1109/TCSS.2023.3344597","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3344597","url":null,"abstract":"The action of spreading false information through fake news articles presents a significant danger to society because it has the ability to shape public opinion with inaccurate facts. This can lead to negative effects, such as reduced trust in institutions and the promotion of conflict, division, and even violence. In this article, a text augmentation technique is introduced as a means of generating new data from preexisting fake news datasets. This approach has the potential to enhance classifier performance by a range of 3%–11%. It can also be utilized to launch a successful attack on trained classifiers, with up to a 90% success rate. However, the success rate of these attacks decreased to less than 28% when the model was retrained with the generated adversarial examples. These results demonstrate the effectiveness of text augmentation as a viable method for detecting fake news and increasing classifier accuracy and performance, as well as its ability to be utilized to perform adversarial machine learning (ML) and improve the resilience of ML algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As one of the most prevalent mental disorders, depression is associated with a high rate of self-harm and suicide, particularly among college students. It is urgently needed to discover prospective cases of depression disorder among college students, enabling timely intervention to reduce its impact on their academic performance and daily lives. This study investigates a method for identifying groups that may have early depressive tendencies through their Internet usage on campus networks. This article proposes a heterogeneous graph attention network (H-GAT) model that incorporates an attention mechanism based on ablation experiments in heterogeneous graphs to analyze the patterns and correlations within the surfing behavior data of students. This model makes full use of the interaction relationships between heterogeneous nodes in the graph to capture the affective tendencies reflected in the cyber-activity patterns. The proposed H-GAT model exhibits excellent performance, with nearly 80% accuracy and recall. Our work offers a potential approach to detect depression on college campuses using nonintrusive methods, which could ultimately contribute to early warnings for both individuals experiencing depression and higher education institutions.
{"title":"Heterogeneous Graph Attention Networks for Depression Identification by Campus Cyber-Activity Patterns","authors":"Minqiang Yang;Zhuoheng Li;Yujie Gao;Chen He;Fuzhan Huang;Wenbo Chen","doi":"10.1109/TCSS.2023.3343689","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3343689","url":null,"abstract":"As one of the most prevalent mental disorders, depression is associated with a high rate of self-harm and suicide, particularly among college students. It is urgently needed to discover prospective cases of depression disorder among college students, enabling timely intervention to reduce its impact on their academic performance and daily lives. This study investigates a method for identifying groups that may have early depressive tendencies through their Internet usage on campus networks. This article proposes a heterogeneous graph attention network (H-GAT) model that incorporates an attention mechanism based on ablation experiments in heterogeneous graphs to analyze the patterns and correlations within the surfing behavior data of students. This model makes full use of the interaction relationships between heterogeneous nodes in the graph to capture the affective tendencies reflected in the cyber-activity patterns. The proposed H-GAT model exhibits excellent performance, with nearly 80% accuracy and recall. Our work offers a potential approach to detect depression on college campuses using nonintrusive methods, which could ultimately contribute to early warnings for both individuals experiencing depression and higher education institutions.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Governance strategies related to platform ecosystems have become a vital issue for developing a smart society, attracting governments’ and practitioners’ attention. Under the consensus of “service as a commodity” and “platform as market,” service providers, platforms, services, and various supply demand matching methods form new supply processes. These elements are continuously and uncertainly changing during supply demand matching, which makes platform ecosystems constantly evolving. However, when multiple supply demand matching methods coexist such as service composition and crossover fusion, dynamic service supply and matching cause dilemmas in the platform ecosystem governance. To this end, this article proposes a model for platform ecosystem evolution with four dynamics: 1) dynamics between ISPs (services) and platforms; 2) dynamics between users and platforms; 3) dynamics among services; and 4) dynamics between services and demands. The model considers multiple supply demand matching methods and considers both fully online services and incompletely online services. Then, according to the market operation law, we design six evaluation indexes such as demand matching rate, service diversity, and market concentration to evaluate the efficiency of the platform market. Finally, a computational experiment system is established to simulate the dynamic supply and matching processes. The experimental results show that reducing the cost of service release can increase the amount of demand and the diversity of services, and the monopoly of digital platforms is a natural trend to improve the efficiency of supply and demand. The model provides a reference for the governance of platform ecosystems and lays a foundation for further research on the value cocreation mechanism of platform ecosystems.
{"title":"A Platform Ecosystem Evolution Model With Service Dynamic Supply and Matching","authors":"Xinyue Zhou;Jianmao Xiao;Xiao Xue;Shizhan Chen;Zhiyong Feng","doi":"10.1109/TCSS.2023.3332064","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3332064","url":null,"abstract":"Governance strategies related to platform ecosystems have become a vital issue for developing a smart society, attracting governments’ and practitioners’ attention. Under the consensus of “service as a commodity” and “platform as market,” service providers, platforms, services, and various supply demand matching methods form new supply processes. These elements are continuously and uncertainly changing during supply demand matching, which makes platform ecosystems constantly evolving. However, when multiple supply demand matching methods coexist such as service composition and crossover fusion, dynamic service supply and matching cause dilemmas in the platform ecosystem governance. To this end, this article proposes a model for platform ecosystem evolution with four dynamics: 1) dynamics between ISPs (services) and platforms; 2) dynamics between users and platforms; 3) dynamics among services; and 4) dynamics between services and demands. The model considers multiple supply demand matching methods and considers both fully online services and incompletely online services. Then, according to the market operation law, we design six evaluation indexes such as demand matching rate, service diversity, and market concentration to evaluate the efficiency of the platform market. Finally, a computational experiment system is established to simulate the dynamic supply and matching processes. The experimental results show that reducing the cost of service release can increase the amount of demand and the diversity of services, and the monopoly of digital platforms is a natural trend to improve the efficiency of supply and demand. The model provides a reference for the governance of platform ecosystems and lays a foundation for further research on the value cocreation mechanism of platform ecosystems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-10DOI: 10.1109/TCSS.2023.3344172
Zena Tian;Guijuan Zhang;Hui Yu;Hong Liu;Dianjie Lu
In emergencies, negative emotion has a significant impact on decision-making during crowd evacuation. Psychological studies suggest that negative emotion in decision-making has a double-edged sword effect. Excessive negative emotion has adverse impacts, such as causing crowd chaos and congestion. Conversely, moderate negative emotion has a positive effect by speeding up crowd movement. However, current researches mainly focus on one aspect which is how to reduce the negative effects of negative emotion on crowd evacuation, while overlooking the benefits of negative emotion. How to fully explore the double-edged sword effect of negative emotion and regulate negative emotion to improve the efficiency of crowd evacuation is still an open issue. To achieve this, we propose the NegEmotion model which considers the positive impact of negative emotion on crowd evacuation, and regulates crowd emotion by controlling knowledge spreading according to Siminov's psychological principle. In this model, the knowledge spreading network (KSN) and the stress emotional contagion network (SECN) are constructed. Based on these networks, we study the evolution process of knowledge spreading and stress emotional contagion, respectively. Next, we formulate the emotional regulation as an optimization problem to maximize the efficiency of crowd evacuation. Then, a heuristic algorithm is used to solve for the optimal emotional regulation strategy. Finally, a crowd simulation system is implemented to verify the effectiveness of our NegEmotion model. The experimental results show that our method is effective to improve the efficiency of crowd evacuation.
{"title":"NegEmotion: Explore the Double-Edged Sword Effect of Negative Emotion on Crowd Evacuation","authors":"Zena Tian;Guijuan Zhang;Hui Yu;Hong Liu;Dianjie Lu","doi":"10.1109/TCSS.2023.3344172","DOIUrl":"https://doi.org/10.1109/TCSS.2023.3344172","url":null,"abstract":"In emergencies, negative emotion has a significant impact on decision-making during crowd evacuation. Psychological studies suggest that negative emotion in decision-making has a double-edged sword effect. Excessive negative emotion has adverse impacts, such as causing crowd chaos and congestion. Conversely, moderate negative emotion has a positive effect by speeding up crowd movement. However, current researches mainly focus on one aspect which is how to reduce the negative effects of negative emotion on crowd evacuation, while overlooking the benefits of negative emotion. How to fully explore the double-edged sword effect of negative emotion and regulate negative emotion to improve the efficiency of crowd evacuation is still an open issue. To achieve this, we propose the NegEmotion model which considers the positive impact of negative emotion on crowd evacuation, and regulates crowd emotion by controlling knowledge spreading according to Siminov's psychological principle. In this model, the knowledge spreading network (KSN) and the stress emotional contagion network (SECN) are constructed. Based on these networks, we study the evolution process of knowledge spreading and stress emotional contagion, respectively. Next, we formulate the emotional regulation as an optimization problem to maximize the efficiency of crowd evacuation. Then, a heuristic algorithm is used to solve for the optimal emotional regulation strategy. Finally, a crowd simulation system is implemented to verify the effectiveness of our NegEmotion model. The experimental results show that our method is effective to improve the efficiency of crowd evacuation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":5.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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