Latest 10 Papers - May 04, 2025
Latest 10 Papers - May 04, 2025
Graph Foundation Model
Graph foundation models have gained significant attention in recent years due to their ability to learn complex graph representations. These models have been applied to various tasks such as graph classification, clustering, and generation. In this section, we will discuss the latest papers on graph foundation models.
Graph Prompt
Graph prompts have been widely used in various applications such as graph classification, clustering, and generation. In this section, we will discuss the latest papers on graph prompts.
| Title | Date | Comment| --- | --- | --- |
| MSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems | 2025-04-25 | |
| Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection | 2025-04-16 | Accepted to AAAI 2025 This work has been submitted to the IEEE TMI for possible publication Peer reviewed and published at NLP4KGc @ Semantics 2024, see original publication at https://ceur-ws.org/Vol-3874/paper3.pdf . Updated Metadata Accepted by SaTML'2025 Not all authors have given their explicit consent Accepted by ICLR 2025Accep...
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Graph Contrastive Learning
Graph contrastive learning has been widely used in various applications such as graph classification, clustering, and generation. In this section, we will discuss the latest papers on graph contrastive learning.
Graph Neural Networks
Graph neural networks have been widely used in various applications such as graph classification, clustering, and generation. In this section, we will discuss the latest papers on graph neural networks.
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Q&A: Graph Foundation Models, Graph Prompts, and Graph Contrastive Learning
In this article, we will answer some of the most frequently asked questions about graph foundation models, graph prompts, and graph contrastive learning.
Q: What is a graph foundation model?
A: A graph foundation model is a type of machine learning model that is designed to learn complex graph representations. These models have been applied to various tasks such as graph classification, clustering, and generation.
Q: What is graph prompt learning?
A: Graph prompt learning is a type of machine learning approach that involves learning to generate graph prompts that can be used to train graph neural networks. Graph prompts are short text sequences that are used to describe the input graph.
Q: What is graph contrastive learning?
A: Graph contrastive learning is a type of machine learning approach that involves learning to contrastively learn graph representations. This approach involves learning to distinguish between different graph representations.
Q: What are the applications of graph foundation models?
A: Graph foundation models have been applied to various tasks such as graph classification, clustering, and generation. They have also been used in various applications such as social network analysis, recommendation systems, and molecular graph analysis.
Q: What are the challenges of graph foundation models?
A: Some of the challenges of graph foundation models include the need for large amounts of training data, the difficulty of learning complex graph representations, and the need for efficient inference algorithms.
Q: What are the applications of graph prompts?
A: Graph prompts have been applied to various tasks such as graph classification, clustering, and generation. They have also been used in various applications such as social network analysis, recommendation systems, and molecular graph analysis.
Q: What are the challenges of graph prompts?
A: Some of the challenges of graph prompts include the need for large amounts of training data, the difficulty of learning complex graph representations, and the need for efficient inference algorithms.
Q: What are the applications of graph contrastive learning?
A: Graph contrastive learning has been applied to various tasks such as graph classification, clustering, and generation. It has also been used in various applications such as social network analysis, recommendation systems, and molecular graph analysis.
Q: What are the challenges of graph contrastive learning?
A: Some of the challenges of graph contrastive learning include the need for large amounts of training data, the difficulty of learning complex graph representations, and the need for efficient inference algorithms.
Q: How can I get started with graph foundation models, graph prompts, and graph contrastive learning?
A: To get started with graph foundation models, graph prompts, and graph contrastive learning, you can start by reading the latest papers on these topics. You can also try implementing some of the algorithms and techniques described in these papers using popular machine learning frameworks such as PyTorch and TensorFlow.
Q: What are some of the popular tools and libraries for graph foundation models, graph prompts, and graph contrastive learning?
A: Some of the popular tools and libraries for graph foundation models, graph prompts, and graph contrastive learning include PyTorch Geometric, TensorFlow Graph, and GraphSAGE.
Q: What are some of the future directions for graph models, graph prompts, and graph contrastive learning?
A: Some of the future directions for graph foundation models, graph prompts, and graph contrastive learning include the development of more efficient inference algorithms, the use of graph foundation models in more applications, and the development of more robust and interpretable graph prompts.
We hope that this Q&A article has been helpful in answering some of the most frequently asked questions about graph foundation models, graph prompts, and graph contrastive learning. If you have any further questions, please don't hesitate to ask.