Recent advancements in knowledge graph embedding have enabled the representation of entities and relations in continuous vector spaces. Performing link prediction on incomplete knowledge graphs using these embeddings has emerged as a challenging ...

Temporal knowledge graph (TKG) multi-hop reasoning is a dominant approach that infers the target entity by walking along the path connecting entities and relations. However, in most TKGs, there are multiple relations related to an identical ...

• Reasoning is dismantled into a relation level for relation reasoning and an entity level for entity reasoning.
• Hybrid time encoding enhances the utilization of timestamp information in reasoning.
• K-means-based reward shaping ...

Existing programming knowledge tracing methods have not deeply explored the relationships between knowledge concepts and programming questions or students’ codes, which leads to insufficient representation of programming questions and students’ ...

Link prediction through continuous dynamic graph neural networks is a challenging endeavour. Previous studies have considered historic interaction sequences among pairs of nodes. However, this approach does not sufficiently model the links ...

• Introduce MTdyg model for dynamic graph link prediction with temporal attention-based encoding.
• Employ multi-scale patch technique to comprehensively segment interaction sequences.
• MTdyg outperforms state-of-the-art baselines on ...

Graph Neural Networks (GNNs) have recently achieved remarkable success in learning graph structures and have been applied in a variety of practical applications such as medical diagnosis, drug discovery, chemical compound synthesis, and traffic ...

• Novel corporate governance drivers for bankruptcy prediction.
• Board of directors' network-based drivers.
• Company relational information-based drivers.
• Two-stage methodology: driver creation and risk prediction.

Corporate bankruptcy prediction is a long-standing topic of interest for a variety of stakeholders. Various prediction methodologies have been proposed to achieve more accurate predictions. So far, most studies have focused on making predictions ...

The neighborhood information theory have been used to evaluate the relevance and redundancy in feature selection for mixed data containing discrete and continuous features. However, existing neighborhood information theory-based feature selection ...

Reversible data hiding in encrypted images (RDHEI) is a challenging task since it requires the complex processing of encrypted images and the lossless recovery after the data extraction. Several RDHEI methods have been proposed to balance the ...

Sensor faults in nuclear power plants (NPPs) have the potential to propagate negative impacts on system stability, leading to false alarms and accident misdiagnosis. Existing methods seldom concurrently consider complex spatial–temporal ...

With the development of Artificial Intelligence for IT Operations (AIOps), numerous software and services are monitored by Key Performance Indicators (KPIs) collection components. Multivariate KPIs, as a type of time series data, are essential ...

The proliferation of online social networks (OSNs) has led to the rampant spread of hate speech. However, traditional detection methods often struggle to effectively detect various forms of hate speech with satisfactory performance, primarily ...

• Hypergraph concepts applied to detect hate speech in online social networks.
• Novel method considers the balance between contextual and user information.
• Rigorous experiments validate the method’s efficiency on public datasets.

The design and development of text-based knowledge graph completion (KGC) methods leveraging textual entity descriptions are at the forefront of research. These methods involve advanced optimization techniques such as soft prompts and contrastive ...

This paper addresses the critical but often overlooked problem of exploring latent sensitive attributes in machine-learning datasets where they are not explicitly present. We propose a method named Object-Relational Causal Inference (ORCI), ...

Joint entity and relation extraction refers to the extraction of entities and their corresponding relationships in the given sentence, which has gained increasing attention in recent years. Some joint extraction models utilize a shared encoder to ...

• GFN introduces a novel approach to joint entity and relation extraction.
• A redundancy strategy is utilized to prioritize recall, reducing error propagation.
• A denoising strategy is designed to filter out entity pairs without ...

Recent advancements in node and graph classification tasks can be attributed to the implementation of contrastive learning and similarity search. Despite considerable progress, these approaches present challenges. The integration of similarity ...

• GCP takes the source graph G s and the pre-trained embeddings (G t ( a ) and G t ( b )) as input to evaluate a downstream task.
• GCP collectively searches for the most promising pair of augmentation for view representation learning.

In this paper, we propose a pioneering approach for blind Hyperspectral Image (HSI) unmixing named Multi-Features Graph Deep Fusion Learning Networks for HSI Unmixing (MF-GDL). Our method leverages the power of multi-feature graph deep fusion ...

• A multi-view graph neural networks is proposed for hyperspectral image unmixing.
• Spectral- and Morphological Profiles-based graphs are built with relevant features.
• Unmixing accuracy is improved by the learned shared latent ...

Missing data pose significant challenges during representation learning of observational data. The incompleteness of data can result in a deterioration of generative performance in disentangled representation learning. Conventional data ...

• A causal disentangled representation learning framework CDRM for missing data is proposed.
• The causal relationships of missing data are recovered by capturing the feature interactions with edge embeddings.
• Causal relationships of ...

The classic problems in itemset mining involve finding frequent itemsets and high-utility itemsets. However, frequent itemset mining has the disadvantage of not paying attention to the profit of products, while high-utility itemset mining does ...

The execution of a business process usually involves the cooperation of many resources (actors) performing various tasks (activities). Generally speaking, the cooperation among actors could significantly influence the efficiency of process ...

Node representation learning plays a crucial role in addressing entity-related tasks in a relational network. However, current methods for node representation learning based on graph neural networks often rely on a layer-by-layer perception ...