Graph Structure Learning

Boosting Graph Structure Learning with Dummy Nodes

In this paper, we use a particular dummy node connecting to all existing vertices without affecting original vertex and edge properties.

Molecular property prediction based on graph structure learning

In this article we propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over ...

Robust Graph Structure Learning with Virtual Nodes Construction

As depicted in Figure 1, the VN-GSL is comprised of three components. The first component involves the utilization of virtual nodes to identify ...

SLAPS: Self-Supervision Improves Structure Learning for Graph ...

A comprehensive experimental study demonstrates that SLAPS scales to large graphs with hundreds of thousands of nodes and outperforms several models that have ...

Graph Structure Learning for Robust Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools in representa- tion learning for graphs. However, recent studies show that GNNs are vulnerable to carefully- ...

Graph Neural Network and Some of GNN Applications - neptune.ai

Basics of Deep Learning for graphs · Unsupervised training: Use only the graph structure: similar nodes have similar embeddings. · Supervised ...

Deep Graph Library

An End-to-End Deep Learning Architecture for Graph Classification, graph ... Heterogeneous graph, Graph neural network, Graph structure. Graphical ...

Graph structure learning based on feature and label consistency

Graph Neural Networks (GNNs) have achieved remarkable success in graph-related tasks by combining node features and graph topology elegantly.

Large Language Models for Graph Structure Learning - OpenReview

Optimized Graph. LLM-Enhanced. Structure Refinement. Figure 1: The model architecture of our proposed GraphEdit framework for graph structure learning. Table 1 ...

Spatially Informed Graph Structure Learning Extracts Insights from ...

STAGUE is an innovative framework that introduces graph structure learning into unsupervised representation learning of spatial ...

Semi-Supervised Graph Structure Learning on Neuromorphic ...

Supervised Graph Structure Learning on Neuromorphic Computers. In. Proceedings of International Conference on Neuromorphic Systems (ICONS '22). ACM, New York ...

Graph structure learning. | Download Scientific Diagram

Download scientific diagram | Graph structure learning. from publication: Multi-Task Time Series Forecasting Based on Graph Neural Networks | Accurate time ...

Incorporating Segment to Syntactic Enhanced Graph Structure ...

Specifically, S2GSL is featured with a segment-aware semantic graph learning and a syntax-based latent graph learning enabling the removal of irrelevant ...

Geometric Deep Learning: Impact of Graph Structure on ... - KiltHub

We explore the impact of the data graph structure on the performance of graph neural networks using real and synthetic data for two graph learning tasks.

Learning Graph Structure from Convolutional Mixtures - alphaXiv

Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively ...

A Survey on Graph Structure Learning: Progress and Opportunities

Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing ...

An adaptive multi-graph neural network with multimodal feature ...

... structural learning mechanism that reconstructs the graph structure through sparse attention after pooling. This approach accurately ...

SE-GSL: A General and Efective Graph Structure Learning ...

Graph Neural Networks (GNNs) are de facto solutions to struc- tural data learning. However, it is susceptible to low-quality and unreliable structure, which has ...

CS224W: Machine Learning with Graphs

... graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools ...

[D] Why are graph neural networks applied to non-graph structured ...

Otherwise it may just be the Geometric Deep Learning hype - Which is also quite interesting in itself lol. Upvote