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ABCD-HN: An Artificial Network Benchmark for Community Detection on Heterogeneous Networks 18th

ABCD-HN: An Artificial Network Benchmark for Community Dete...

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18th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2023

作者： Liu, Junjie Guo, Kun Wu, Ling Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science/College of Software Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Community detection is essential for identifying cohesive groups in complex networks. Artificial benchmarks are critical for evaluating community detection algorithms, offering controlled environments with known community structures. However, existing benchmarks mainly focus on homogeneous networks and overlook the unique characteristics of heterogeneous networks. This paper proposes a novel artificial benchmark, called ABCD-HN (Artificial Network Benchmark for Community Detection on Heterogeneous Networks), for community detection in heterogeneous networks. This benchmark enables the generation of artificial heterogeneous networks with controllable community quantity, node quantity, and community complexity. Additionally, an evaluation framework for artificial heterogeneous networks is proposed to assess their effectiveness. Experimental results demonstrate the effectiveness and usability of ABCD-HN as a benchmark for artificial heterogeneous networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heterogeneous networks

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Reinforcement Learning Driven Moth-flame Optimization Algorithm for Solving Numerical Optimization Problems 26

Reinforcement Learning Driven Moth-flame Optimization Algori...

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Zhao, Fuqing Wang, Qiaoyun Xu, Zesong Lanzhou University of Technology School of Computer and Communication Lanzhou China

ISBN: (纸本)9798350331684

Moth-flame optimization (MFO) algorithm has received a lot of attention recently, due to its simple structure and easy coding. Researchers have demonstrated that the original MFO algorithm suffers from the drawbacks of insufficient variety, slow convergence speed, and readily sliding into local optimum, which are brought about by the imbalance between local and global search. Reinforcement learning driven moth-flame optimization (RLMFO) algorithm is designed to correct these issues. Opposition learning is employed to broaden the variety of the initial population. Reinforcement learning is introduced to direct the local and global search process of the algorithm. A strategy pool containing Gaussian mutation (GM), Cauchy mutation (CM), Lévy mutation (LM), and elite strategy (ES) is created to hold strategies with various functions. RLMFO is verified on the benchmark test suite in CEC 2017. RLMFO performs better than cutting-edge algorithms according to experimental findings. © 2023 IEEE.

关键词： Reinforcement learning

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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“It’s Teamwork!” – Cooperation and Collaboration in social Casual Exergames

“It’s Teamwork!” – Cooperation and Collaboration in Soci...

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2025 CHI conference on Human Factors in computing Systems, CHI EA 2025

作者： Orädd, Helena Berglund, Erik Kristoffer Berglund, Aseel Department of Computer and Information Science Linköping University Linköping Sweden

ISBN: (纸本)9798400713958

Exergames offer an engaging approach to combat sedentary lifestyles and promote physical activity. While the benefits of multiplayer gaming are established, the nuanced effects of different cooperative modes remain underexplored for exergames. This study investigates the impact of cooperation (parallel work towards a shared goal) versus collaboration (joint effort towards a shared goal) on player experience in casual exergames. Results revealed that collaboration significantly improved both positive and negative affect, fostered stronger interpersonal connections, and was preferred by most players, while cooperation often was perceived as competitive and led to higher exertion levels. These findings illuminate the complex interplay between cooperation, competition, and collaboration in exergames. © 2025 Copyright held by the owner/author(s).

关键词： Casual exergames Collaboration Cooperation Player experience

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FLBC-DSS: Federated Learning-Enabled Blockchain-Based Data Sharing Scheme for IIoT 11

FLBC-DSS: Federated Learning-Enabled Blockchain-Based Data S...

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11th IEEE International conference on Behavioural and social computing, BESC 2024

作者： Yao, Yan Li, Guangshun Zhang, Huihui Feng, Shanshan Yan, Biwei Cao, Jian School of Computer Science Qufu Normal University Rizhao China Rizhao-Qufu Normal University Joint Technology Transfer Center Rizhao China School of Computer Engineering Weifang University Weifang China Qilu University of Technology Shandong Academy of Sciences Jinan China School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Scienceand Technology Shandong University Qingdao China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798331531904

This paper introduces the Federated Learning-Empowered Blockchain-based Data Sharing Scheme (FLBC-DSS), a novel framework designed to enhance data security and efficiency in Industrial Internet of Things (IIoT) environments. By integrating federated learning with blockchain technology, FLBC-DSS addresses the critical challenges of data privacy and unauthorized access, promoting secure, decentralized data sharing across various industrial sectors. Utilizing zk-SNARKs, the scheme provides a mechanism for anonymous data verification without compromising data integrity. Performance evaluations conducted using Hyperledger Fabric and SecureBoost demonstrate significant improvements in prediction accuracy and computational efficiency compared to traditional approaches. The implementation of non-cooperative game theory further illustrates the economic benefits of FLBC-DSS, highlighting its potential to increase profitability through strategic data sharing. The results affirm the effectiveness of FLBC-DSS in leveraging advanced cryptographic and machine learning techniques to foster a secure, transparent, and efficient digital ecosystem for IIoT. © 2024 IEEE.

关键词： Federated learning

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Accelerating Unsupervised Federated Graph Neural Networks via Semi-asynchronous Communication 18th

Accelerating Unsupervised Federated Graph Neural Networks vi...

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18th CCF conference on computer supported cooperative work and social computing, ChineseCSCW 2023

作者： Liao, Yuanming Wu, Duanji Lin, Pengyu Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Graph neural networks have shown excellent performance in many fields owing to their powerful processing ability of graph data. In recent years, federated graph neural network has become a reasonable solution due to the enactment of privacy-related regulations. However, frequent communication between the coordinator and participants in federated graph neural network results in longer model training time and consumes many communication resources. To address this challenge, in this paper, we propose a novel semi-asynchronous federated graph learning communication protocol that simultaneously alleviates the negative impact of stragglers(slow participants) and accelerate the training process in the unsupervised federated graph neural network scenario. First, the weighted enforced synchronization strategy is intended to preserve the information carried by stragglers while preventing their stale models from harming the global model update. Second, the adaptive local update strategy is developed to make the local model of the participant with poor computing performance as close as possible to the global model. Experiments combine federated learning with graph contrastive learning. The results demonstrate that our proposed protocol outperforms the existing protocols in real-world networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet protocols

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Management, Cooperation, and Sustainability: Unpacking the Data Practices of Housing cooperatives 25

Management, Cooperation, and Sustainability: Unpacking the D...

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2025 CHI conference on Human Factors in computing Systems, CHI 2025

作者： Verma, Priyanka Rifat, Mohammad Rashidujjaman Sabie, Samar Faculty of Information University of Toronto Toronto ON Canada Computer Science University of Toronto Toronto ON Canada Institute of Communication Culture Information Technology University of Toronto Mississauga ON Canada

ISBN: (纸本)9798400713941

Despite significant work in HCI on understanding the role of data tools for non-profits and grassroots communities, there has been limited focus on cooperatives. This paper examines the role of financial, social, and building upkeep data in a non-profit cooperative housing organization in Toronto, Canada (alias named NXI). Through a 16-month-long ethnographic study, including 24 interviews, we investigate the role of and tensions in data practices related to NXI's daily maintenance and operations, cooperation, and sustainability. We find that NXI's current data practices are functional and meaningful - sometimes requiring team workarounds - in the short term. However, various tensions and deficiencies in data practices hamper NXI's sustainability. By contextualizing the temporal affordances of data, we propose design implications for data tools to align effectively with the practices of cooperatives and enhance organizational sustainability. Finally, we discuss how data designers and researchers, organizations or grassroots communities, and financial technology designers can benefit from our work, especially with regard to the maintenance and sustainability of small-scale organizations. © 2025 Copyright held by the owner/author(s). Publication rights licensed to acm.

关键词： cooperatives Data Practices Financial Practices Housing

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An Epidemic Model Based on Intra- and Inter-group Interactions 26

An Epidemic Model Based on Intra- and Inter-group Interactio...

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26th International conference on computer supported cooperative work in Design, CSCWD 2023

作者： Geng, Wencong Zhang, Guijuan Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250101 China

ISBN: (纸本)9798350331684

The global spread of COVID-19 causes great losses to human society. Accurate calculation of the scale of epidemic spread is of great significance for the implementation of corresponding epidemic prevention measures. However, the existing method ignores the group formed by social relations of the population, which reduces the accuracy of the epidemic spread number calculation. In this paper, we propose an epidemic model based on intra- and inter-group interactions. Firstly, we construct a dual network model of epidemic spread based on intra- and inter-group interactions. The network describes how epidemics spread intra- and inter-group. To capture the intergroup influences, we construct a model for social mobility to calculate the inter-group spread rate. Secondly, we propose a computational model for the epidemic spread. We calculate the infection probability of groups in the upper layer network by using a continuous-time Markov chain (CTMC). We describe a dynamic evolution of the intra-group infection in the underlying network based on the mean field equation. And the number of infections in the population is calculated by integrating intra- and inter-group effects. Finally, we implement an epidemic spread simulation system to visualize the spread process. The experimental results show that the model can analyze the epidemic spread process more accurately. © 2023 IEEE.

关键词： COVID-19

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CSCW 2017 - Companion of the 2017 acm conference on computer supported cooperative work and social computing

CSCW 2017 - Companion of the 2017 ACM Conference on Computer...

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2017 acm conference on computer supported cooperative work and social computing, CSCW 2017

ISBN: (纸本)9781450346887

The proceedings contain 100 papers. The topics discussed include: professional network analytics platform for enterprise collaboration;agile research studios: orchestrating communities of practice to advance research training;augmenting media literacy with automatic characterization of news along pragmatic dimensions;manifesting the Cyborg via techno body modification: from human computer interaction to integration;understanding and designing around users' interaction with hidden algorithms in sociotechnical systems;design of a home telepresence robot system for supporting childcare;how developers make design decisions about user' privacy: the place of professional communities and organizational climate;beyond knowledge acquisition: medical device training as a cooperative process;negative electronic word-of-mouth can support product recommendation: experimental investigation;conceptualization of computer-supported collaborative SenseMaking;personas and scenarios to design technologies for north Korean defectors with depression;recruiting messages matter: message strategies to attract citizen scientists;puzzle space: a distributed tangible puzzle for long distance couples;action ADE: enabling cross-setting communication to prevent adverse drug events;we can query more than we can tell: facilitating collaboration through query-driven knowledge-sharing;crowdsourcing law and policy: a design-thinking approach to crowd-civic systems;and hacking and making at time-bounded events: current trends and next steps in research and event design.

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