Mengmeng Yang, Youyang Qu, Thilina Ranbaduge, Chandra Thapa, Nazatul Haque Sultan, Ming Ding, Hajime Suzuki, Wei Ni, Sharif Abuadbba, David Smith, Paul Tyler, Josef Pieprzyk, Thierry Rakotoarivelo, Xinlong Guan, Sirine Mrabet
The vision for 6G aims to enhance network capabilities, supporting an intelligent digital ecosystem where artificial intelligence (AI) is a key. However, the expansion of 6G raises critical security and privacy concerns due to the increased integration of IoT devices, edge computing, and AI. This survey provides a comprehensive overview of 6G protocols with a focus on security and privacy, identifying risks that have not been experienced in preceding 5G systems, and presenting mitigation strategies. While many vulnerabilities from earlier generations persist, the introduction of AI/ML introduces novel risks like model inversion and malicious manipulation of AI. Vulnerabilities in emerging personal IoT networks and autonomous vehicles are also underscored, where falsified command signaling or privacy leakage can pose safety and ethical concerns. The survey also discusses the transition toward lattice-based, post-quantum encryption standards, and identifies limitations in current security frameworks and calls for new, dynamic approaches tailored to 6G’s complexity. Close collaboration among stakeholders, including governments, industry, and researchers, is indispensable to developing robust standards, secure architectures, and risk assessment frameworks that address AI, quantum threats, and privacy at scale.
{"title":"From 5G to 6G: A Survey on Security, Privacy, and Standardization Pathways","authors":"Mengmeng Yang, Youyang Qu, Thilina Ranbaduge, Chandra Thapa, Nazatul Haque Sultan, Ming Ding, Hajime Suzuki, Wei Ni, Sharif Abuadbba, David Smith, Paul Tyler, Josef Pieprzyk, Thierry Rakotoarivelo, Xinlong Guan, Sirine Mrabet","doi":"10.1145/3785467","DOIUrl":"https://doi.org/10.1145/3785467","url":null,"abstract":"The vision for 6G aims to enhance network capabilities, supporting an intelligent digital ecosystem where artificial intelligence (AI) is a key. However, the expansion of 6G raises critical security and privacy concerns due to the increased integration of IoT devices, edge computing, and AI. This survey provides a comprehensive overview of 6G protocols with a focus on security and privacy, identifying risks that have not been experienced in preceding 5G systems, and presenting mitigation strategies. While many vulnerabilities from earlier generations persist, the introduction of AI/ML introduces novel risks like model inversion and malicious manipulation of AI. Vulnerabilities in emerging personal IoT networks and autonomous vehicles are also underscored, where falsified command signaling or privacy leakage can pose safety and ethical concerns. The survey also discusses the transition toward lattice-based, post-quantum encryption standards, and identifies limitations in current security frameworks and calls for new, dynamic approaches tailored to 6G’s complexity. Close collaboration among stakeholders, including governments, industry, and researchers, is indispensable to developing robust standards, secure architectures, and risk assessment frameworks that address AI, quantum threats, and privacy at scale.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"34 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 digital landscape increasingly relies on data exchange, underscoring the importance of Digital Identity Management (DIM) systems. However, current models face vulnerabilities, leading to the demand for more secure, decentralized, and user-centric solutions. Self-sovereign Identity (SSI) has emerged as a promising paradigm supported by blockchain technology, granting users control over their digital identities. This paper reviews existing research and commercial SSI solutions, assessing their feasibility and effectiveness. We thoroughly explore SSI’s concept, structure, and components while addressing challenges like scalability, usability, Quantum computing-based attacks and corresponding resistance mechanisms, data storage, and governance. Additionally, we highlight recent advancements and identify research gaps, suggesting future directions for enhanced DIM solutions.
{"title":"Self-Sovereign Identity as a Secure and Trustworthy Approach to Digital Identity Management: A Comprehensive Survey","authors":"Efat Fathalla, Mohamed Azab, ChunSheng Xin, Hongyi Wu","doi":"10.1145/3785466","DOIUrl":"https://doi.org/10.1145/3785466","url":null,"abstract":"The digital landscape increasingly relies on data exchange, underscoring the importance of Digital Identity Management (DIM) systems. However, current models face vulnerabilities, leading to the demand for more secure, decentralized, and user-centric solutions. Self-sovereign Identity (SSI) has emerged as a promising paradigm supported by blockchain technology, granting users control over their digital identities. This paper reviews existing research and commercial SSI solutions, assessing their feasibility and effectiveness. We thoroughly explore SSI’s concept, structure, and components while addressing challenges like scalability, usability, Quantum computing-based attacks and corresponding resistance mechanisms, data storage, and governance. Additionally, we highlight recent advancements and identify research gaps, suggesting future directions for enhanced DIM solutions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"23 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chaoming Shi, Haomeng Xie, Zheng Yan, Laurence T. Yang
Blockchain is a decentralized ledger with a secure and immutable chain structure. The advanced attributes of blockchain, including decentralization, anonymity, transparency, and zero trust support, have positioned it as a transformative technology across different areas of expertise, like medicine, finance, and the Internet of Things (IoT). Nonetheless, blockchain’s progress has been constrained in various aspects, revealing inefficiency, privacy, high transaction fees, and challenges with on-chain storage. To address these limitations, off-chain technology has emerged as a solution by moving computation and storage overhead away from the blockchain. However, a comprehensive survey on off-chain schemes is lacking in the current literature. In this paper, we conduct a thorough survey on off-chain technologies. We first introduce the fundamental concepts and characteristics of both blockchain and off-chain technologies. Furthermore, we establish a thorough taxonomy of off-chain technologies based on distinct application scenarios. We put forth a series of evaluation criteria, based on which we seriously review and analyze the existing off-chain schemes to assess their strengths and limitations. Conclusively, we outline a list of open issues and propose promising future research directions based on our thorough review and analysis on off-chain technologies.
{"title":"A Survey on Off-chain Technologies","authors":"Chaoming Shi, Haomeng Xie, Zheng Yan, Laurence T. Yang","doi":"10.1145/3765897","DOIUrl":"https://doi.org/10.1145/3765897","url":null,"abstract":"Blockchain is a decentralized ledger with a secure and immutable chain structure. The advanced attributes of blockchain, including decentralization, anonymity, transparency, and zero trust support, have positioned it as a transformative technology across different areas of expertise, like medicine, finance, and the Internet of Things (IoT). Nonetheless, blockchain’s progress has been constrained in various aspects, revealing inefficiency, privacy, high transaction fees, and challenges with on-chain storage. To address these limitations, off-chain technology has emerged as a solution by moving computation and storage overhead away from the blockchain. However, a comprehensive survey on off-chain schemes is lacking in the current literature. In this paper, we conduct a thorough survey on off-chain technologies. We first introduce the fundamental concepts and characteristics of both blockchain and off-chain technologies. Furthermore, we establish a thorough taxonomy of off-chain technologies based on distinct application scenarios. We put forth a series of evaluation criteria, based on which we seriously review and analyze the existing off-chain schemes to assess their strengths and limitations. Conclusively, we outline a list of open issues and propose promising future research directions based on our thorough review and analysis on off-chain technologies.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"152 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock market. Despite the importance of these methods, there is a scarcity of scholarly works that systematically synthesize previous studies from the perspective of external knowledge types. Specifically, the external knowledge can be modeled in different data structures, which we group into non-graph-based formats and graph-based formats: 1) non-graph-based knowledge captures contextual information and multimedia descriptions specifically associated with an individual stock; 2) graph-based knowledge captures interconnected and interdependent information in the stock market. This survey paper aims to provide a systematic and comprehensive description of methods for acquiring external knowledge from various unstructured data sources and then incorporating it into stock price prediction models. We also explore fusion methods for combining external knowledge with historical price features. Moreover, this paper includes a compilation of relevant datasets and delves into potential future research directions in this domain.
{"title":"Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey","authors":"Liping Wang, Jiawei Li, Lifan Zhao, Zhizhuo Kou, Xiaohan Wang, Xinyi Zhu, Hao Wang, Yanyan Shen, Chen Lei","doi":"10.1145/3773079","DOIUrl":"https://doi.org/10.1145/3773079","url":null,"abstract":"Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock market. Despite the importance of these methods, there is a scarcity of scholarly works that systematically synthesize previous studies from the perspective of external knowledge types. Specifically, the external knowledge can be modeled in different data structures, which we group into non-graph-based formats and graph-based formats: 1) <jats:italic toggle=\"yes\">non-graph-based knowledge</jats:italic> captures contextual information and multimedia descriptions specifically associated with an individual stock; 2) <jats:italic toggle=\"yes\">graph-based knowledge</jats:italic> captures interconnected and interdependent information in the stock market. This survey paper aims to provide a systematic and comprehensive description of methods for acquiring external knowledge from various unstructured data sources and then incorporating it into stock price prediction models. We also explore fusion methods for combining external knowledge with historical price features. Moreover, this paper includes a compilation of relevant datasets and delves into potential future research directions in this domain.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"20 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 rapid advancement of Intelligent Transportation Systems (ITS) has led to a paradigm shift towards the adoption of Connected Autonomous Vehicles (CAVs). In recent years, CAVs have emerged as a prominent research focus due to their potential to reduce road traffic accidents caused by human error, optimize traffic flow, create new economic opportunities, and enhance travel convenience. However, the increasing demand for compute and delay-sensitive applications, such as real-time navigation and sensor data processing, exceeds the capabilities of current onboard vehicle resources. Consequently, task offloading has gained significant attention, allowing certain computational tasks generated by CAVs operations to be offloaded to external cloud or edge servers. The existing review literature has been limited in its focus on task offloading techniques specifically for CAVs architecture. Therefore, this study aims to present a comprehensive survey on task offloading in CAVs through a systematic review guided by key research questions. We first provide a technical background and then propose a broad coverage taxonomy of existing literature, analyzing promising solutions such as Machine Learning (ML) and heuristic-based techniques. In addition, we present a taxonomy of execution environments, metrics, and datasets. Finally, we highlight key research challenges and future trends, providing valuable insights for advancing task offloading in CAVs architecture.
{"title":"Task Offloading for CAVs Edge Computing Environment: Taxonomy, Critical Review, and Future Road Map","authors":"Bhoopendra Kumar, Aditya Bhardwaj, Dinesh Prasad Sahu","doi":"10.1145/3783984","DOIUrl":"https://doi.org/10.1145/3783984","url":null,"abstract":"The rapid advancement of Intelligent Transportation Systems (ITS) has led to a paradigm shift towards the adoption of Connected Autonomous Vehicles (CAVs). In recent years, CAVs have emerged as a prominent research focus due to their potential to reduce road traffic accidents caused by human error, optimize traffic flow, create new economic opportunities, and enhance travel convenience. However, the increasing demand for compute and delay-sensitive applications, such as real-time navigation and sensor data processing, exceeds the capabilities of current onboard vehicle resources. Consequently, task offloading has gained significant attention, allowing certain computational tasks generated by CAVs operations to be offloaded to external cloud or edge servers. The existing review literature has been limited in its focus on task offloading techniques specifically for CAVs architecture. Therefore, this study aims to present a comprehensive survey on task offloading in CAVs through a systematic review guided by key research questions. We first provide a technical background and then propose a broad coverage taxonomy of existing literature, analyzing promising solutions such as Machine Learning (ML) and heuristic-based techniques. In addition, we present a taxonomy of execution environments, metrics, and datasets. Finally, we highlight key research challenges and future trends, providing valuable insights for advancing task offloading in CAVs architecture.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"9 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benjamin Carrion Schafer, Baharealsadat Parchamdar
Approximate Computing in hardware design has emerged as an alternative way to further reduce the power consumption of integrated circuits (ICs) by trading off errors at the output with simpler, and more efficient logic. So far, the main approaches in approximate computing have been to simplify the hardware circuit by applying different approximation primitives of different aggressiveness to the original hardware description until the maximum error threshold is met. Multiple of these primitives can also be combined to obtain better results. These primitives are often applied at different VLSI design stages to maximize their effect. Because of the importance of this topic, there exists a very large body of work and multiple surveys have tried to cover all of it. In this work we take a different approach and concentrate only on approximation computing techniques applied at the High-Level Synthesis (HLS) stage of the VLSI design flow. The reason for this is that approximations applied at the highest possible level of VLSI design abstraction also have the highest impact on the resultant circuit. Moreover, HLS is finally being widely embraced by hardware designers, and this work aims at presenting practical examples of how the different approximation primitives can be easily applied using commercial HLS tools. We finally present some typical pitfalls that designers should avoid when using approximate computing and point to some future direction in this area.
{"title":"Approximate Computing in High-Level Synthesis: From Survey to Practical Implementation","authors":"Benjamin Carrion Schafer, Baharealsadat Parchamdar","doi":"10.1145/3785334","DOIUrl":"https://doi.org/10.1145/3785334","url":null,"abstract":"Approximate Computing in hardware design has emerged as an alternative way to further reduce the power consumption of integrated circuits (ICs) by trading off errors at the output with simpler, and more efficient logic. So far, the main approaches in approximate computing have been to simplify the hardware circuit by applying different approximation primitives of different aggressiveness to the original hardware description until the maximum error threshold is met. Multiple of these primitives can also be combined to obtain better results. These primitives are often applied at different VLSI design stages to maximize their effect. Because of the importance of this topic, there exists a very large body of work and multiple surveys have tried to cover all of it. In this work we take a different approach and concentrate only on approximation computing techniques applied at the High-Level Synthesis (HLS) stage of the VLSI design flow. The reason for this is that approximations applied at the highest possible level of VLSI design abstraction also have the highest impact on the resultant circuit. Moreover, HLS is finally being widely embraced by hardware designers, and this work aims at presenting practical examples of how the different approximation primitives can be easily applied using commercial HLS tools. We finally present some typical pitfalls that designers should avoid when using approximate computing and point to some future direction in this area.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"6 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Shirui Pan, Qingsong Wen
Diffusion models have been widely used in time series and spatio-temporal data, enhancing generative, inferential, and downstream capabilities. These models are applied across diverse fields such as healthcare, recommendation, climate, energy, audio, and traffic. By separating applications for time series and spatio-temporal data, we offer a structured perspective on model category, task type, data modality, and practical application domain. This study aims to provide a solid foundation for researchers and practitioners, inspiring future innovations that tackle traditional challenges and foster novel solutions in diffusion model-based data mining tasks and applications. For more detailed information, we have open-sourced a repository.
{"title":"A Survey on Diffusion Models for Time Series and Spatio-Temporal Data","authors":"Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Shirui Pan, Qingsong Wen","doi":"10.1145/3783986","DOIUrl":"https://doi.org/10.1145/3783986","url":null,"abstract":"Diffusion models have been widely used in time series and spatio-temporal data, enhancing generative, inferential, and downstream capabilities. These models are applied across diverse fields such as healthcare, recommendation, climate, energy, audio, and traffic. By separating applications for time series and spatio-temporal data, we offer a structured perspective on model category, task type, data modality, and practical application domain. This study aims to provide a solid foundation for researchers and practitioners, inspiring future innovations that tackle traditional challenges and foster novel solutions in diffusion model-based data mining tasks and applications. For more detailed information, we have open-sourced a repository.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"110 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145711161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weibang Dai, Xiaogang Chen, Houpeng Chen, Sannian Song, Shunfen Li, Tao Hong, Zhitang Song
For decades, memory-based computation has been overshadowed by processor-centric paradigms. However, memory-based computation offers distinct advantages, including high-speed operation and energy efficiency. As a representative and powerful type of memory-based computation, lookup table (LUT)-based computing has seen a resurgence in interest. Recent advancements in memory technologies, particularly cost reduction in memories and the rise of emerging non-volatile memories (NVMs), have spurred widespread adoption of LUT-based approaches. In this paper, we first trace the historical evolution of LUT-based computation, then systematically analyze its modern applications across two domains: (1) software implementations, including LUT-based function evaluation and LUT-based neural networks; and (2) hardware architectures, such as LUT in FPGA and LUT-based processing-in-memory (PIM) systems. Finally, we discuss how NVMs could unlock new opportunities for next-generation LUT-based computing.
{"title":"Lookup Table-based Computing: A Survey from Software Implementations to Hardware Architectures","authors":"Weibang Dai, Xiaogang Chen, Houpeng Chen, Sannian Song, Shunfen Li, Tao Hong, Zhitang Song","doi":"10.1145/3779417","DOIUrl":"https://doi.org/10.1145/3779417","url":null,"abstract":"For decades, memory-based computation has been overshadowed by processor-centric paradigms. However, memory-based computation offers distinct advantages, including high-speed operation and energy efficiency. As a representative and powerful type of memory-based computation, lookup table (LUT)-based computing has seen a resurgence in interest. Recent advancements in memory technologies, particularly cost reduction in memories and the rise of emerging non-volatile memories (NVMs), have spurred widespread adoption of LUT-based approaches. In this paper, we first trace the historical evolution of LUT-based computation, then systematically analyze its modern applications across two domains: (1) software implementations, including LUT-based function evaluation and LUT-based neural networks; and (2) hardware architectures, such as LUT in FPGA and LUT-based processing-in-memory (PIM) systems. Finally, we discuss how NVMs could unlock new opportunities for next-generation LUT-based computing.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"115 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jelena Smiljanić, Christopher Blöcker, Anton Holmgren, Daniel Edler, Magnus Neuman, Martin Rosvall
Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, detecting community structures in complex networks requires selecting a community detection method among a multitude of alternatives with different network representations, community interpretations, and underlying mechanisms. This tutorial focuses on a popular community detection method called the map equation and its search algorithm Infomap. The map equation framework for community detection describes communities by analyzing dynamic processes on the network. Thanks to its flexibility, the map equation provides extensions that can incorporate various assumptions about network structure and dynamics. To help decide if the map equation is a suitable community detection method for a given complex system and problem at hand – and which variant to choose – we review the map equation’s theoretical framework and guide users in applying the map equation to various research problems.
{"title":"Community Detection with the Map Equation and Infomap: Theory and Applications","authors":"Jelena Smiljanić, Christopher Blöcker, Anton Holmgren, Daniel Edler, Magnus Neuman, Martin Rosvall","doi":"10.1145/3779648","DOIUrl":"https://doi.org/10.1145/3779648","url":null,"abstract":"Real-world networks have a complex topology comprising many elements often structured into communities. Revealing these communities helps researchers uncover the organizational and functional structure of the system that the network represents. However, detecting community structures in complex networks requires selecting a community detection method among a multitude of alternatives with different network representations, community interpretations, and underlying mechanisms. This tutorial focuses on a popular community detection method called the map equation and its search algorithm Infomap. The map equation framework for community detection describes communities by analyzing dynamic processes on the network. Thanks to its flexibility, the map equation provides extensions that can incorporate various assumptions about network structure and dynamics. To help decide if the map equation is a suitable community detection method for a given complex system and problem at hand – and which variant to choose – we review the map equation’s theoretical framework and guide users in applying the map equation to various research problems.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"34 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ignacio Marco-Pérez, Beatriz Pérez, Angel Luis Rubio Garcia, María A. Zapata
Today, our data is not only stored on personal computers, but is managed by many devices, from cell phones or watches to smart TVs, and stored in remote repositories (usually referred to as “the cloud”). In this new context, defining what exactly “data deletion” is becomes a challenge, especially considering the many different scenarios in which it is becoming more increasingly important. This is the case, for example, of the “right to be forgotten” established by regulations such as the European General Data Protection Regulation (GDPR) or the deletion of data used as a source to feed machine learning processes, the long-term effects of which are very difficult to estimate. This work reviews the various terminology used when dealing with data deletion and analyzes the different fields and technologies to which it is related. We conclude by offering a structured discussion of key takeaways, lessons learned, and future research directions.
{"title":"The Many Faces of Data Deletion: On the Significance and Implications of Deleting Data","authors":"Ignacio Marco-Pérez, Beatriz Pérez, Angel Luis Rubio Garcia, María A. Zapata","doi":"10.1145/3779299","DOIUrl":"https://doi.org/10.1145/3779299","url":null,"abstract":"Today, our data is not only stored on personal computers, but is managed by many devices, from cell phones or watches to smart TVs, and stored in remote repositories (usually referred to as “the cloud”). In this new context, defining what exactly “data deletion” is becomes a challenge, especially considering the many different scenarios in which it is becoming more increasingly important. This is the case, for example, of the “right to be forgotten” established by regulations such as the European General Data Protection Regulation (GDPR) or the deletion of data used as a source to feed machine learning processes, the long-term effects of which are very difficult to estimate. This work reviews the various terminology used when dealing with data deletion and analyzes the different fields and technologies to which it is related. We conclude by offering a structured discussion of key takeaways, lessons learned, and future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"10 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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