The Advantage of Evidential Attributes in Social Networks
LARODEC, Higher Institute of Management（高等管理学院 LARODEC 实验室）；DRUID, IRISA, University of Rennes 1（雷恩第一大学 IRISA 研究所 DRUID 团队）；Northwestern Polytechnical University（西北工业大学）
Salma Ben Dhaou, Kuang Zhou, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane
备注：
20th International Conference on Information Fusion, Jul 2017, Xi'an, China
发表信息：
20th International Conference on Information Fusion, Jul 2017, Xi'an, China. 2017
Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；School of Cyber Science and Technology, Beihang University（北京航空航天大学网络空间安全学院）；Salesforce（赛富时）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks
Jilin University Changchun China；Adelaide University Adelaide Australia；Zhejiang University of Technology Hangzhou China；RMIT Univeristy Melbourne Australia；RMIT University Melbourne Australia；Jinan University \& TAL Education Group Guangzhou China；Jilin University；Adelaide University；Zhejiang University of Technology；RMIT Univeristy；RMIT University；Jinan University \& TAL Education Group
Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区智能系统工程学院）；Baidu Inc（百度公司）；Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, the School of Electronic Information and Electrical Engineering, Dalian University of Technology（大连理工大学电子信息与电气工程学部 工业装备智能控制与优化教育部重点实验室）
Community Search in Time-dependent Road-social Attributed Networks
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
University of California, Berkeley（加州大学伯克利分校）；University of Cambridge（剑桥大学）；University of Washington（华盛顿大学）；University of Michigan（密歇根大学）；University of Toronto（多伦多大学）
Social Perception of Faces in a Vision-Language Model
California Institute of Technology（加州理工学院）；ETH Zurich（苏黎世联邦理工学院）；Computational Social Science（计算社会科学）；Swiss Data Science Center（瑞士数据科学中心）；Empa（瑞士联邦材料科学与技术研究院）
