Latest Articles for Link Prediction

A new model improves link prediction in multiplex social networks.

New framework enhances link prediction in knowledge graphs using language models.

Research presents a model to identify future influencers for effective marketing.

Study examines how missing connections affect link prediction accuracy across networks.

A new framework enhances graph representation learning across multiple tasks.

This work focuses on improving link prediction accuracy with innovative sampling methods.

Exploring how Knowledge Graphs enhance understanding in AI's decision-making.

A new method improves link prediction by addressing noisy and incomplete data.

A novel method using binary vectors to represent language concepts efficiently.

Link2Doc enhances link prediction by merging text and graph structure.

This article discusses how GRSNN enhances graph reasoning tasks using synaptic delay.

LinkLogic provides clarity and reliability for link prediction in knowledge graphs.

A new method enhances the transparency of knowledge graph embedding models.

A new method to assess the effectiveness of Graph Neural Networks across varied network structures.

Introducing GENIE, a watermarking method for safeguarding Graph Neural Networks against theft.

A look into how links form and evolve in social networks.

A new framework explains node embeddings using simple graph features.

This article examines challenges in link prediction due to data distribution shifts.

A novel approach to enhance link prediction in various graph types.

New model enhances the study of changing relationships over time.

A new method combines rule mining and link prediction to enrich knowledge graphs.

A new benchmark addresses biases in link prediction evaluations for better real-world applications.

This study focuses on inferring hidden network connections using random walks.

A new method enhances predictions of drug-side effects using data-driven approaches.

A new method enhances video object segmentation by leveraging contextual relationships.

Learn how DKGs enhance financial decision-making through trend analysis.

CoCN enhances graph representation learning through innovative message-passing techniques.

This study investigates how numerical literals affect link prediction in knowledge graphs.

Introducing CNES for efficient link prediction in changing networks.

Wav2graph creates knowledge graphs from spoken language for improved AI understanding.

Discover how Graph Convolutional Networks analyze complex data in various fields.

Snapshot ensembles enhance link prediction in knowledge graphs through model diversity.

GATH enhances knowledge graph completion using advanced attention mechanisms for better accuracy.

Exploring how embeddings transform information processing in machines.

Path-LLM offers new ways to create meaningful graph embeddings for diverse applications.

A new model enhances link prediction in knowledge graphs using textual descriptions.

A new method enhances the accuracy of knowledge graph predictions.

Study community formation in networks to gain insights into social behavior.

Learn how links form in networks through popularity and similarity.

A study on Graphair's effectiveness in fair graph representations and link prediction.