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Bi-CLKT: Bi-graph contrastive learning based knowledge tracing
Abstract The goal of Knowledge Tracing (KT) is to estimate how well students have
mastered a concept based on their historical learning of related exercises. The benefit of …
mastered a concept based on their historical learning of related exercises. The benefit of …
Social network analysis using deep learning: applications and schemes
Online social networks (OSNs) are part of daily life of human beings. Millions of users are
connected through online social networks. Due to very large number of users and huge …
connected through online social networks. Due to very large number of users and huge …
Multi-center federated learning: clients clustering for better personalization
Personalized decision-making can be implemented in a Federated learning (FL) framework
that can collaboratively train a decision model by extracting knowledge across intelligent …
that can collaboratively train a decision model by extracting knowledge across intelligent …
Auxiliary signal-guided knowledge encoder-decoder for medical report generation
Medical reports have significant clinical value to radiologists and specialists, especially
during a pandemic like COVID. However, beyond the common difficulties faced in the …
during a pandemic like COVID. However, beyond the common difficulties faced in the …
Transo: a knowledge-driven representation learning method with ontology information constraints
Abstract Representation learning techniques for knowledge graphs (KGs) are crucial for
constructing knowledge-driven decisions in complex network data application scenarios …
constructing knowledge-driven decisions in complex network data application scenarios …
Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning
Exploitation time is an essential factor for vulnerability assessment in cybersecurity
management. In this work, we propose an integrated consecutive batch learning framework …
management. In this work, we propose an integrated consecutive batch learning framework …
Collaborative representation learning for nodes and relations via heterogeneous graph neural network
Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly
suitable for characterizing real-world complex systems. In recent years, due to their strong …
suitable for characterizing real-world complex systems. In recent years, due to their strong …
JointE: Jointly utilizing 1D and 2D convolution for knowledge graph embedding
Abstract Knowledge graph embedding is a popular method to predict missing links for
knowledge graphs by projecting entities and relations into continuous low-dimension …
knowledge graphs by projecting entities and relations into continuous low-dimension …
Effective deep attributed network representation learning with topology adapted smoothing
Attributed networks are ubiquitous in the real world, such as social networks. Therefore,
many researchers take the node attributes into consideration in the network representation …
many researchers take the node attributes into consideration in the network representation …
Low rank matrix factorization algorithm based on multi-graph regularization for detecting drug-disease association
Detecting potential associations between drugs and diseases plays an indispensable role in
drug development, which has also become a research hotspot in recent years. Compared …
drug development, which has also become a research hotspot in recent years. Compared …