Search Results

In the field of machine learning and pattern recognition, the use of binary correlation indices is essential for accurate prediction and modelling. This work presents a novel evolutionary method to address the problem of discovering binary ...

• We study a general formulation for a family of domain-specific binary correlations.
• We develop meta-correlations, representing classes of similarity indices.
• We use evolutionary optimization enabling the best correlation for given ...

Knowledge Graph (KG) is an essential research field in graph theory, but its inherent incompleteness and sparsity influence its performance in several fields. Knowledge Graph Reasoning (KGR) aims to ameliorate those problems by mining new ...

Knowledge graphs provide credible and structured knowledge for downstream tasks such as information retrieval. Nevertheless, the ubiquitous incompleteness of knowledge graphs often limits the performance of applications. To address the ...

• Knowledge graph completion with triple-level interaction promotes capture factual global semantics.
• Hard negative sampling reduces computational requirements for contrast learning.
• Inference with fusion degree information ...

Convolutional Neural Networks (CNNs) have demonstrated effectiveness in knowledge graph embedding, but existing CNN-based methods encounter two main challenges. Firstly, CNN-based models with simple architectures are unable to extract latent ...

Rank aggregation is a vital tool in facilitating decision-making processes that consider multiple criteria or attributes. While in many applications, the available ranked lists are often limited and quite partial for various reasons. This ...

• We address the problem of rank aggregation with limited information.
• We present a networked representation of ranking information.
• We employ the link prediction to mine potential ranking information.

Link prediction is a critical task within the realm of graph machine learning. While recent advancements mainly emphasize node representation learning, the rich information encapsulated within edges, proven advantageous in various graph-related ...

• Incorporating edge information for link prediction.
• Edge-level instead of node-level contrastive learning.
• Leveraging MLPs for edge representation.
• Edge representation can improve link prediction.

Finding future or missing associations is an essential problem in complex systems because of its numerous application areas. These applications use link prediction methods to provide services to their customers. Our work proposes a novel ...

Temporal Knowledge Graph (TKG) reasoning aiming at forecasting future events based on historical snapshots distributed over timestamps. Referred to as extrapolation in numerous studies, this aspect of research in KG has garnered significant ...

• A novel method of multi-graph learning for temporal knowledge graph reasoning.
• An expressive graph aggregator to model spatial–temporal interactions.
• An adaptive way to balance fuse various entity features.

This study presents a novel link prediction model for the analysis and prediction of collaboration networks in the context of H2020 projects. This model incorporates various databases and expands the list of variables used in the analysis. In ...

• A new link prediction model is proposed for H2020 collaboration network analysis.
• Economic and corporate indicators are applied to construct a network-based database.
• Generic and nongeneric methods for link prediction are applied.

Graph Neural Network(GNN) has achieved remarkable performance in classification tasks due to its strong distinctive power of different graph topologies. However, traditional GNNs face great limitations in link prediction tasks since they learn ...

Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to ...

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• An automated pipeline that extracts food-chemical information from literature.
• A food-chemical knowledge graph with 46 % triplets not in public databases.
• 6 food-chemical potentially novel associations discovered by link ...

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 ...

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 ...

In the cloud computing domain, significant strides have been made in performance prediction for cloud workflows, yet link prediction for cloud workflows remains largely unexplored. This paper introduces a novel challenge: joint node and link ...

The paper focuses on link prediction in time-evolving social networks, i.e., networks whose structure changes over time. The aim of link prediction is to forecast future connections between pairs of unconnected nodes by analyzing existing ...

Link prediction determines if there is an edge between two unconnected nodes in a complex network using known information, such as network topology and/or node semantic attributes. However, existing link prediction methods primarily rely on ...

Heterogeneous network link prediction is a hot topic in the analysis of networks. It aims to predict missing links in the network by utilizing the rich semantic information present in the heterogeneous network, thereby enhancing the effectiveness ...

Recent research has been actively conducted on fault diagnosis in hydrogen extraction systems using artificial intelligence. However, existing studies have not considered the characteristics of hydrogen extractors, where multiple processes form a ...

Link prediction, a crucial research topic in complex network studies, involves estimating the likelihood of links between two nodes based on known network information. This area has garnered widespread attention due to its theoretical ...

• Introducing LGS-GNN, a novel method for noise reduction and consistency enhancement in graph data for link prediction tasks.
• Implementing three attack strategies on graph data to generate adversarial samples.
• Theoretical ...

Knowledge graph representation learning entails transforming entities and relationships within a knowledge graph into vectors to enhance downstream tasks. The rise of pre-trained language models has recently promoted text-based approaches for ...