School of Artificial Intelligence, OPtics and ElectroNics (iOPEN)（人工智能学院（iOPEN））；Northwestern Polytechnical University（西北工业大学）；Institute of Artificial Intelligence (TeleAI)（人工智能研究所（TeleAI））
Mulin Chen, Bocheng Wang, Jiaxin Zhong, Zongcheng Miao, Xuelong Li
属性缺失图聚类已成为一项重要的无监督任务，该任务下仅部分节点的属性向量可用，而图结构保持完整。相关模型通常遵循“补全-优化”的两步范式，但多数补全方法无法捕捉与类别相关的语义信息，导致补全结果对聚类任务而言并非最优。此外，现有优化策略通过图重构来优化学习到的嵌入，却忽略了部分属性与图不相关的事实。针对上述问题，我们构建了带可靠优化的面向聚类生成式补全（Clustering-oriented Generative Imputation with reliable Refinement, CGIR）模型。具体而言，该模型估计子簇分布以精准揭示类别特异性特征，并约束生成对抗模块的采样空间，从而推动待补全节点与正确聚类对齐。随后，多个子簇被合并以指导所提出的边注意力网络，该网络可识别每个类别的边级属性，避免图重构中的冗余属性干扰整体嵌入的优化。综上，CGIR将属性缺失图聚类拆分为子簇的搜索与合并过程，在统一框架下指导节点补全与嵌入优化。大量实验证明，CGIR相较于现有最优竞品具备性能优势。
英文摘要：
Attribute-missing graph clustering has emerged as a significant unsupervised task, where only attribute vectors of partial nodes are available and the graph structure is intact. The related models generally follow the two-step paradigm of imputation and refinement. However, most imputation approaches fail to capture class-relevant semantic information, leading to sub-optimal imputation for clustering. Moreover, existing refinement strategies optimize the learned embedding through graph reconstruction, while neglecting the fact that some attributes are uncorrelated with the graph. To remedy the problems, we establish the Clustering-oriented Generative Imputation with reliable Refinement (CGIR) model. Concretely, the subcluster distributions are estimated to reveal the class-specific characteristics precisely, and constrain the sampling space of the generative adversarial module, such that the imputation nodes are impelled to align with the correct clusters. Afterwards, multiple subclusters are merged to guide the proposed edge attention network, which identifies the edge-wise attributes for each class, so as to avoid the redundant attributes in graph reconstruction from disturbing the refinement of overall embedding. To sum up, CGIR splits attribute-missing graph clustering into the search and mergence of subclusters, which guides to implement node imputation and refinement within a unified framework. Extensive experiments prove the advantages of CGIR over state-of-the-art competitors.
Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset
University of Macau（澳门大学）；Northeast Normal University（东北师范大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Oxford（牛津大学）；Dalian Maritime University（大连海事大学）
Processed data are insightful, and crude data are obtuse. A serious threat to data reliability is missing values. Such data leads to inaccurate analysis and wrong predictions. We propose an efficient technique to impute the missing value in the dataset based on correlation called FCMI (Feature Correlation based Missing Data Imputation). We have considered the correlation of the attributes of the dataset, and that is our central idea. Our proposed algorithm picks the highly correlated attributes of the dataset and uses these attributes to build a regression model whose parameters are optimized …
Data values in a dataset can be missing or anomalous due to mishandling or human error. Analysing data with missing values can create bias and affect the inferences. Several analysis methods, such as principle components analysis or singular value decomposition, require complete data. Many approaches impute numeric data and some do not consider dependency of attributes on other attributes, while some require human intervention and domain knowledge. We present a new algorithm for data imputation based on different data type values and their association constraints in data, which are not handled…
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；AI Lab, Tencent（腾讯AI实验室）
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation a burning issue. Until recently, many works notice that GNNs are coupled with spectral concentration, which means the spectrum obtained by GNNs concentrates on a local part in spectral domain, e.g., low-frequency due to oversmoothing issue. As a consequence, GNNs may be seriously flawed for reconstructing graph attributes as graph spectral concentration tends to cause a low imputation precision. In this work, we pres…
Models can expose sensitive information about their training data. In an attribute inference attack, an adversary has partial knowledge of some training records and access to a model trained on those records, and infers the unknown values of a sensitive feature of those records. We study a fine-grained variant of attribute inference we call \emph{sensitive value inference}, where the adversary's goal is to identify with high confidence some records from a candidate set where the unknown attribute has a particular sensitive value. We explicitly compare attribute inference with data imputation t…
Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graph
School of Statistics, East China Normal University（华东师范大学统计学院）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；School of Global Public Health, New York University（纽约大学全球公共卫生学院）
National University of Defense Technology（国防科技大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Hainan University（海南大学）；Hainan blockchain technology engineering research center（海南区块链技术工程研究中心）
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world. Unfortunately, graphs usually suffer from being absent due to privacy-protecting policies or copyright restrictions during data collection. The absence of graph data can be roughly categorized into attribute-incomplete and attribute-missing circumstances. Specifically, attribute-incomplete indicates that a part of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that the whole attribute vectors of partial nodes are mis…
National Remote Sensing Centre (ISRO), India（印度国家遥感中心（ISRO））
Decision making from data involves identifying a set of attributes that contribute to effective decision making through computational intelligence. The presence of missing values greatly influences the selection of right set of attributes and this renders degradation in classification accuracies of the classifiers. As missing values are quite common in data collection phase during field experiments or clinical trails appropriate handling would improve the classifier performance. In this paper we present a review of recently developed missing value imputation algorithms and compare their perfor…
Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphs
College of Computer Science and Technology（计算机科学与技术学院）；National University of Defense Technology（国防科技大学）；Xiangjiang Laboratory（湘江实验室）；Hainan University（海南大学）；Changsha University（长沙大学）；Qilu University of Technology（齐鲁工业大学）；North University of China（北方工业大学）
Bilkent University（比尔肯特大学）；Aselsan（阿瑟尔桑）；Middle East Technical University（中东技术大学）
We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose i) a novel algorithm to efficiently separate, i.e., detect and localize, possible corruptions from a given suspicious data instance and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As a generalization to Euclidean distance, we also propose a novel distance measure, which is based on the ranked deviations among the data a…
Lahore University of Management Science（拉合尔管理科学大学）；Georgia State University（佐治亚州立大学）；Massey University（梅西大学）；Hazara University（哈扎拉大学）；National University of Ireland Galway（爱尔兰国立高威大学）
Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further harmfully affect fairness. Many imputation methods are proposed to deal with missing data. However, the effect of missing data imputation on fairness is not studied well. In this paper, we analyze the effect on fairness in the context of graph dat…
For some or all of the data instances a number of independent-world clustering issues suffer from incomplete data characterization due to losing or absent attributes. Typical clustering approaches cannot be applied directly to such data unless pre-processing by techniques like imputation or marginalization. We have overcome this drawback by utilizing a Sentenced Discrepancy Measure which we refer to as the Attribute Weighted Penalty based Discrepancy (AWPD). Using the AWPD measure, we modified the K-MEANS++ and Scalable K-MEANS++ for clustering algorithm and k Nearest Neighbor (kNN) for classi…
IBM Corporation（国际商业机器公司）；North Carolina State University（北卡罗来纳州立大学）
Opinionated text often involves attributes such as authorship and location that influence the sentiments expressed for different aspects. We posit that structural and semantic correspondence is both prevalent in opinionated text, especially when associated with attributes, and crucial in accurately revealing its latent aspect and sentiment structure. However, it is not recognized by existing approaches. We propose Trait, an unsupervised probabilistic model that discovers aspects and sentiments from text and associates them with different attributes. To this end, Trait infers and leverages stru…
Massachusetts Institute of Technology（麻省理工学院）；BITS Pilani Goa Campus（BITS Pilani果阿校区）
The problem of missing data has been persistent for a long time and poses a major obstacle in machine learning and statistical data analysis. Past works in this field have tried using various data imputation techniques to fill in the missing data, or training neural networks (NNs) with the missing data. In this work, we propose a simple yet effective approach that clusters similar input features together using hierarchical clustering and then trains proportionately split neural networks with a joint loss. We evaluate this approach on a series of benchmark datasets and show promising improvemen…
Mondragon Unibertsitatea - Goi Eskola Politeknikoa（蒙德拉贡大学-高等理工学院）
Attribute Oriented Induction (AOI) is a data mining algorithm used for extracting knowledge of relational data, taking into account expert knowledge. It is a clustering algorithm that works by transforming the values of the attributes and converting an instance into others that are more generic or ambiguous. In this way, it seeks similarities between elements to generate data groupings. AOI was initially conceived as an algorithm for knowledge discovery in databases, but over the years it has been applied to other areas such as spatial patterns, intrusion detection or strategy making. In this …
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Griffith University（格里菲斯大学）；Monash University（莫纳什大学）；Squirrel AI（松鼠AI）；IDSIA（瑞士IDSIA人工智能实验室）；Università della Svizzera italiana（意大利语瑞士大学）；Politecnico di Milano（米兰理工大学）；The Chinese University of Hong Kong（香港中文大学）
CentraleSupelec（中央理工-高等电力学院）；Univ. Paris Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；University of Lorraine（洛林大学）；RTE France（法国输电公司）；Princeton University（普林斯顿大学）
Data clustering is an instrumental tool in the area of energy resource management. One problem with conventional clustering is that it does not take the final use of the clustered data into account, which may lead to a very suboptimal use of energy or computational resources. When clustered data are used by a decision-making entity, it turns out that significant gains can be obtained by tailoring the clustering scheme to the final task performed by the decision-making entity. The key to having good final performance is to automatically extract the important attributes of the data space that ar…
University of New South Wales（新南威尔士大学）；University of Science and Technology of China（中国科学技术大学）；The Chinese University of Hong Kong（香港中文大学）
Predicting the link between two nodes is a fundamental problem for graph data analytics. In attributed graphs, both the structure and attribute information can be utilized for link prediction. Most existing studies focus on transductive link prediction where both nodes are already in the graph. However, many real-world applications require inductive prediction for new nodes having only attribute information. It is more challenging since the new nodes do not have structure information and cannot be seen during the model training. To solve this problem, we propose a model called DEAL, which cons…
On Transferring, Merging, and Splitting Task-Oriented Network Digital Twins
Department of Computer Science, North Carolina State University（计算机科学系，北卡罗来纳州立大学）；Department of Computer Science and Engineering, University of Louisville（计算机科学与工程系，路易斯维尔大学）；Department of Electrical and Computer Engineering and Frost Institute for Data Science and Computing, University of Miami（电气与计算机工程系及弗罗斯特数据科学与计算研究所，迈阿密大学）
How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（eBRAIN实验室，工程系，纽约大学阿布扎比分校）；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（量子与拓扑系统中心（CQTS），NYUAD研究院）；Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome（信息工程、电子与电信系，罗马萨皮恩扎大学）
The ability to estimate joint, conditional and marginal probability distributions over some set of variables is of great utility for many common machine learning tasks. However, estimating these distributions can be challenging, particularly in the case of data containing a mix of discrete and continuous variables. This paper presents a non-parametric method for estimating these distributions directly from a dataset. The data are first represented as a graph consisting of object nodes and attribute value nodes. Depending on the distribution to be estimated, an appropriate eigenvector equation …
