Instituto Federal do Norte de Minas Gerais (IFNMG)（米纳斯吉拉斯北部联邦学院（IFNMG））；Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）
Jeancarlo Campos Leão, Alberto H. F. Laender, Pedro O. S. Vaz de Melo
备注：
16 pages, 4 figures. This paper has been submitted to Journal of Information and Data Management (JIDM). arXiv admin note: substantial text overlap with arXiv:1909.09903
Community detection is a key task to further understand the function and the structure of complex networks. Therefore, a strategy used to assess this task must be able to avoid biased and incorrect results that might invalidate further analyses or applications that rely on such communities. Two widely used strategies to assess this task are generally known as structural and functional. The structural strategy basically consists in detecting and assessing such communities by using multiple methods and structural metrics. On the other hand, the functional strategy might be used when ground truth data are available to assess the detected communities. However, the evaluation of communities based on such strategies is usually done in experimental configurations that are largely susceptible to biases, a situation that is inherent to algorithms, metrics and network data used in this task. Furthermore, such strategies are not systematically combined in a way that allows for the identification and mitigation of bias in the algorithms, metrics or network data to converge into more consistent results. In this context, the main contribution of this article is an approach that supports a robust quality evaluation when detecting communities in real-world networks. In our approach, we measure the quality of a community by applying the structural and functional strategies, and the combination of both, to obtain different pieces of evidence. Then, we consider the divergences and the consensus among the pieces of evidence to identify and overcome possible sources of bias in community detection algorithms, evaluation metrics, and network data. Experiments conducted with several real and synthetic networks provided results that show the effectiveness of our approach to obtain more consistent conclusions about the quality of the detected communities.
Community Notes are Vulnerable to Rater Bias and Manipulation
Observatory on Social Media, Indiana University（印第安纳大学社会媒体观测站）；Luddy School of Informatics, Computing, and Engineering, Indiana University（印第安纳大学拉迪信息、计算与工程学院）
本文系统评估了 Community Notes 算法，发现其易受评分者偏差和少数恶意评分者操纵，导致有用笔记被抑制，凸显了改进众包事实核查机制的必要性。
Non-Dissipative Graph Propagation for Non-Local Community Detection
School of Engineering Mathematics and Technology（工程数学与技术学院）；Technology University of Bristol Bristol, UK（布里斯托尔技术大学（英国））；Dept. of Computer Science University of Pisa Pisa, Italy（计算机科学系，帕尔米斯大学（意大利））
Unnoticeable Community Deception via Multi-objective Optimization
Department of Electrical Engineering, City University of Hong Kong（香港城市大学电子工程系）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）
Privacy-Preserving Community Detection for Locally Distributed Multiple Networks
School of Mathematics, Northwest University, China（中国西北大学数学学院）；University of Pennsylvania, U.S.A.（美国宾夕法尼亚大学）；School of Management, Xi’an Jiaotong University, China（中国西安交通大学管理学院）
