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Graph neural networks (GNN) have achieved remarkable success in various domains, yet incomplete node attribute data can significantly impair their performance. Graph completion learning (GCL) methods have been developed to address this issue, aiming to reconstruct missing node attributes based on existing structural relationships. However, the accuracy of these reconstructions is highly dependent on the quality of the initial graph structure, which often contains errors and inaccuracies. This paper proposes the evolving graph structure (EGS) framework for semi-supervised node classification with missing attributes. EGS dynamically reconstructs the attributes of the nodes and updates the graph structure through an alternating optimization approach. Specifically, we introduce a Dirichlet Energy function with dual constraints to formulate the objective function, which jointly optimizes node structure relationships and attribute reconstruction. Extensive experiments on five benchmark datasets, with different missing rates, and with seven GNN variants demonstrate the effectiveness of EGS, achieving state-of-the-art performance compared to existing GCL methods.

Heterogeneous Graph Neural Networks(HGNNs), as an effective tool for mining heterogeneous graphs, have achieved remarkable performance on series of real-world applications. Yet, HGNNs are limited in their mining power as they require all nodes to have complete and reliable attributes. It is usually unrealistic since the attributes of many nodes in reality are inevitably missing or noisy. Existing methods usually take imputation schemes to complete missing attributes, in which topology information is ignored, leading to suboptimal performance. In this work, we study the consistency-based augmentation on heterogeneous graphs, completing the missing attributes and improving original attributes simultaneously, and propose a novel generic architecture−Learning on Heterogeneous Graph Neural Networks with Consistency-based Augmentation(CAHGNN), including random sampling, attribute augmentation and consistency training. In graph augmentation, to ensure attributes sensible and accurate, the attention mechanism is adopted to complete attributes under the guidance of the topological relationship between nodes. Extensive experiments on three benchmark datasets demonstrate the superior performance of CAHGNN over state-of-the-art baselines on semi-supervised node classification.

Heterogeneous graphs are widely employed in applications such as social networks, recommendation systems, and bioinformatics. However, node attributes in real-world heterogeneous graphs are often missing or corrupted, which substantially degrades representation quality and downstream task performance. Existing approaches typically rely on deterministic imputation or static masking schemes, limiting their ability to model the uncertainty induced by attribute missingness and the complex multi-relational dependencies present in real-world heterogeneous graphs. To address these challenges, we propose HGGAE (Heterogeneous Graph Generative Autoencoder), a generative autoencoder framework based on a perturbation–recovery paradigm for heterogeneous graphs with incomplete attributes. HGGAE explicitly models attribute missingness as a controllable perturbation process, and performs progressive attribute restoration and representation learning through the joint design of a schedulable noise generator and relation-specific structural perturbation modules. Unlike traditional masking-based methods, HGGAE adaptively adjusts perturbation intensity during training, enabling more effective modeling of the stochastic nature of attribute degradation. To improve training efficiency, HGGAE adopts a sparse-target objective and a local reconstruction design, which reduce the supervision and gradient-accumulation cost of attribute reconstruction, while the overall computation remains dominated by full-graph message passing in the encoder. Experiments on four benchmark heterogeneous graph datasets demonstrate that HGGAE achieves overall strong and competitive performance on node classification, achieving up to 7.8% Macro-F1 and 8.5% Micro-F1 gains on IMDB, while delivering competitive or superior performance on Yelp, ACM, and DBLP. These results validate the effectiveness, robustness, and generalization capability of HGGAE under attribute-missing scenarios.

Graph Neural Networks (GNNs) typically assume the presence of node attributes to capture interactions in a graph structure. However, real-world graph data often has incomplete or completely-missing attribute information. GNN approaches to dealing with incomplete attributes are widely implemented, yet there is a paucity of research on graphs without attributes. DeepWalk and node2vec can be exploited to generate artificial attributes in Graph Convolutional Networks (GCN). However, the stochastic nature of random walks disrupts consistent performance. Furthermore, it introduces a degree bias, which causes the over-sampling of hub nodes and the under-representation of low-degree nodes. To address this limitation, we propose Walk Graph Convolutional Networks (WalkGCN) for generating artificial node attributes. WalkGCN employs a biased sampling strategy that mitigates degree-induced bias during node sequence generation. It increases the sampling frequency of leaf nodes to balance the training data. The node embeddings generated in the preceding phase are employed as artificial attributes for each node. The graph with artificial attributes is then fed to the vanilla graph convolutional networks model, which performs node classification. The results of extensive experiments on three graph datasets demonstrate that WalkGCN effectively generates artificial attributes that are independent of the hyperparameters or randomness of the random walk. Furthermore, the proposed model outperforms a variety of baseline models. The code is available at https://github.com/mincheol-shin-cau/WalkGCN .

A main challenge of attribute-based access control (ABAC) is the handling of missing information. Several studies have shown that the way standard ABAC mechanisms, e.g. based on XACML, handle missing information is flawed, making ABAC policies vulnerable to attribute-hiding attacks. Recent work has addressed the problem of missing information in ABAC by introducing the notion of extended evaluation, where the evaluation of a query considers all queries that can be obtained by extending the initial query. This method counters attribute-hiding attacks, but a naïve implementation is intractable, as it requires an evaluation of the whole query space. In this paper, we present a framework for the extended evaluation of ABAC policies. The framework relies on Binary Decision Diagram (BDDs) data structures for the efficient computation of the extended evaluation of ABAC policies. We also introduce the notion of query constraints and attribute value power to avoid evaluating queries that do not represent a valid state of the system and to identify which attribute values should be considered in the computation of the extended evaluation, respectively. We illustrate our framework using three real-world policies, which would be intractable with the original method but which are analyzed in seconds using our framework.

The problems of data abnormalities and missing data are puzzling the traditional multi-modal heterogeneous big data clustering. In order to solve this issue, a multi-view heterogeneous big data clustering algorithm based on improved Kmeans clustering is established in this paper. At first, for the big data which involve heterogeneous data, based on multi view data analyzing, we propose an advanced Kmeans algorithm on the base of multi view heterogeneous system to determine the similarity detection metrics. Then, a BP neural network method is used to predict the missing attribute values, complete the missing data and restore the big data structure in heterogeneous state. Last, we ulteriorly propose a data denoising algorithm to denoise the abnormal data. Based on the above methods, we construct a framework namely BPK-means to resolve the problems of data abnormalities and missing data. Our solution approach is evaluated through rigorous performance evaluation study. Compared with the original algorithm, both theoretical verification and experimental results show that the accuracy of the proposed method is greatly improved.

This research develops a taxonomy of alphanumeric brand names (ABs) based on the alignment between the brand names and their links to products and attributes. Five empirical studies reveal that ABs have systematic effects on consumers' product choices, moderated by consumers' need for cognition, the availability of product attribute information, and the taxonomic category of the AB. In an identical choice set, the choice share of a product option whose brand name takes a higher versus lower numeric portion (e.g., X-200 versus X-100) increases, and it is preferred more even when it is objectively inferior to other choice alternatives. Consumers with low need for cognition use \"the higher, the better\" heuristic to select options labeled with ABs and choose brands with higher numeric portions. Consumers with high need for cognition process ABs more systematically and make inferences about attribute values based on brand name—attribute correlations. The effects of ABs on consumer preferences are prevalent for most technical products, even when consumers do not know the product category or meanings of attributes.

This study estimates consumers’ willingness to pay (WTP) for sustainability turfgrass attributes such as low-input and stress-tolerance attributes, while considering potential trade-off relationships between aesthetic attributes and sustainability attributes. To address our objectives, our study conducts a choice experiment and estimates two mixed logit models. The first model includes low-input, winter kill, and shade-tolerance attributes as predictor variables, and the second model extends the first model by adding interaction terms between the aesthetic and sustainability attributes. Another choice experiment is conducted under water policies with various water rate increase and watering restriction scenarios. Results from the mixed logit models show that, overall, higher low-input cost reduction, less winter-damaged, and more shade-tolerant grasses are preferred, and that the direct effect of aesthetic attributes on consumers’ preferences is strong, but the indirect effects represented by the interaction terms are generally statistically insignificant. Our results indicate that consumers like to have a pretty lawn, but no strong consideration is given to the aesthetics of their lawn when selecting low-input and stress-tolerant turfgrasses. Our choice experiment under water policy scenarios suggests that water pricing is more effective than watering restriction in increasing consumer demand for water-conserving turfgrasses.

The handling of missing attribute values remains a challenging and problematic issue in data analysis. Imputation techniques are key procedures used to deal with missing attribute values. However, although these methods are widely used, they cause data bias. Rough set theory, a unique mathematical tool for decision making under uncertainty, overcomes this problem by properly adjusting the relationships. Rough sets are often preferred in both classification and sorting problems. The aim of sorting problems is to sort the objects in the decision table (DT) from best to worst and/or to select the best one. For this purpose, it is necessary to obtain a pairwise comparison table (PCT) from the DT. However, in the presence of missing values, the transformation from DT to PCT is not feasible because there are no ranking methods in the literature for sorting problems based on rough sets. To address this limitation, this paper presents a way to transform from DT to PCT and introduces a generalization of the relation belonging to the “do not care” type of missing values in the dominance-based rough set approach (DRSA) to the decision support tool jRank. We also adapted the DomLem algorithm to enable it to work in PCT with missing values. We applied our method step by step to a decision table with 11 objects and investigated the effect of missing values. The experimental results showed that our proposed approach captures the semantics of ‘do not care’ type missing values.

In many application settings, data have missing entries, which makes subsequent analyses challenging. An abundant literature addresses missing values in an inferential framework, aiming at estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing values appear in both training and test data. We first rewrite classic missing values results for this setting. We then show the consistency of two approaches, test-time multiple imputation and single imputation in prediction. A striking result is that the widely-used method of imputing with a constant prior to learning is consistent when missing values are not informative. This contrasts with inferential settings where mean imputation is frowned upon as it distorts the distribution of the data. The consistency of such a popular simple approach is important in practice. Finally, to contrast procedures based on imputation prior to learning with procedures that optimize the missing-value handling for prediction, we consider decision trees. Indeed, decision trees are among the few methods that can tackle empirical risk minimization with missing values, due to their ability to handle the half-discrete nature of incomplete variables. After comparing empirically different missing values strategies in trees, we recommend using the “missing incorporated in attribute” method as it can handle both non-informative and informative missing values.