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Found 9 papers, 5 papers with code
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges.
Unifying Graph Contrastive Learning with Flexible Contextual Scopes
To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short).
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss.
Service resource allocation problem in the IoT driven personalized healthcare information platform
With real-time monitoring of the personalized healthcare condition, the IoT wearables collect the health data and transfer it to the healthcare information platform.
Improving Personalised Physical Activity Recommendation on the mHealth Information Service Using Deep Reinforcement Learning
The behavioural factors include changing fitness preferences, adherence issues, and uncertainty about future fitness outcomes, which may all lead to a decline in the quality of the mHealth information services.
Deep Learning for Embodied Vision Navigation: A Survey
A navigation agent is supposed to have various intelligent skills, such as visual perceiving, mapping, planning, exploring and reasoning, etc.
Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.
Online Semi-Supervised Concept Drift Detection with Density Estimation
The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distribution of X without affecting the posterior probabilities.
Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering
A new type of clusters called mass-connected clusters is formally defined.
