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Found 12 papers, 7 papers with code
Constrained Layout Generation with Factor Graphs
This paper addresses the challenge of object-centric layout generation under spatial constraints, seen in multiple domains including floorplan design process.
Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing
We show how implicit GNN layers can be viewed as the fixed-point equation of a Dirichlet energy minimization problem and give conditions under which it may suffer from over-smoothing during training (OST) and inference (OSI).
Fairness-aware Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown great power in various domains.
SyNDock: N Rigid Protein Docking via Learnable Group Synchronization
Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
What Has Been Enhanced in my Knowledge-Enhanced Language Model?
We conduct experiments to verify that our GCS can indeed be used to correctly interpret the KI process, and we use it to analyze two well-known knowledge-enhanced LMs: ERNIE and K-Adapter, and find that only a small amount of factual knowledge is integrated in them.
$p$-Laplacian Based Graph Neural Networks
Graph neural networks (GNNs) have demonstrated superior performance for semi-supervised node classification on graphs, as a result of their ability to exploit node features and topological information simultaneously.
Robust Dynamic Network Embedding via Ensembles
It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
This paper aims to provide a theoretical framework to understand GNNs, specifically, spectral graph convolutional networks and graph attention networks, from graph signal denoising perspectives.
Learning Topological Representation for Networks via Hierarchical Sampling
To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network.
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Representation Learning for Heterogeneous Information Networks via Embedding Events
Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.
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