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Found 6 papers, 6 papers with code
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
With a synergized framework of LLM and KG mutually enhancing each other, we first leverage LLM to construct an evolving AD-specific knowledge graph (KG) sourced from AD-related scientific literature, and then we utilize a coarse-to-fine sampling method with a novel self-aware knowledge retrieval approach to select appropriate knowledge from the KG to augment LLM inference capabilities.
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
Leveraging these modified representations, DEGNN subsequently addresses downstream tasks, ensuring robustness against noise present in both edges and node features of real-world graphs.
MUSE: Music Recommender System with Shuffle Play Recommendation Enhancement
Recommender systems have become indispensable in music streaming services, enhancing user experiences by personalizing playlists and facilitating the serendipitous discovery of new music.
S-Mixup: Structural Mixup for Graph Neural Networks
Existing studies for applying the mixup technique on graphs mainly focus on graph classification tasks, while the research in node classification is still under-explored.
MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation
The long-tailed problem is a long-standing challenge in Sequential Recommender Systems (SRS) in which the problem exists in terms of both users and items.
LTE4G: Long-Tail Experts for Graph Neural Networks
After having trained an expert for each balanced subset, we adopt knowledge distillation to obtain two class-wise students, i. e., Head class student and Tail class student, each of which is responsible for classifying nodes in the head classes and tail classes, respectively.
