Search Results for author: Sitao Luan

Found 17 papers, 5 papers with code

Representation Learning on Heterophilic Graph with Directional Neighborhood Attention

no code implementations3 Mar 2024 Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang

However, since it only incorporates information from immediate neighborhood, it lacks the ability to capture long-range and global graph information, leading to unsatisfactory performance on some datasets, particularly on heterophilic graphs.

Graph Attention Representation Learning

On Addressing the Limitations of Graph Neural Networks

no code implementations22 Jun 2023 Sitao Luan

This report gives a summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines future directions to explore.

MUDiff: Unified Diffusion for Complete Molecule Generation

no code implementations28 Apr 2023 Chenqing Hua, Sitao Luan, Minkai Xu, Rex Ying, Jie Fu, Stefano Ermon, Doina Precup

Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.

Drug Discovery valid

When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability

1 code implementation25 Apr 2023 Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup

Homophily principle, i. e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks.

Node Classification Stochastic Block Model

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks

no code implementations21 Dec 2022 Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood information of nodes.

Node Classification

When Do We Need Graph Neural Networks for Node Classification?

no code implementations30 Oct 2022 Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Doina Precup

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i. i. d.)

Classification Inductive Bias +1

Revisiting Heterophily For Graph Neural Networks

1 code implementation14 Oct 2022 Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup

ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers.

Inductive Bias Node Classification on Non-Homophilic (Heterophilic) Graphs

Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey

no code implementations24 May 2022 Chenqing Hua, Sitao Luan, Qian Zhang, Jie Fu

Graph Neural Networks (GNNs) are new inference methods developed in recent years and are attracting growing attention due to their effectiveness and flexibility in solving inference and learning problems over graph-structured data.

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks

no code implementations20 Aug 2020 Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information.

Graph Classification Node Classification

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks

no code implementations20 Aug 2020 Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup

The performance limit of Graph Convolutional Networks (GCNs) and the fact that we cannot stack more of them to increase the performance, which we usually do for other deep learning paradigms, are pervasively thought to be caused by the limitations of the GCN layers, including insufficient expressive power, etc.

Revisit Policy Optimization in Matrix Form

no code implementations19 Sep 2019 Sitao Luan, Xiao-Wen Chang, Doina Precup

In tabular case, when the reward and environment dynamics are known, policy evaluation can be written as $\bm{V}_{\bm{\pi}} = (I - \gamma P_{\bm{\pi}})^{-1} \bm{r}_{\bm{\pi}}$, where $P_{\bm{\pi}}$ is the state transition matrix given policy ${\bm{\pi}}$ and $\bm{r}_{\bm{\pi}}$ is the reward signal given ${\bm{\pi}}$.

Model-based Reinforcement Learning

META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation

2 code implementations25 Apr 2019 Mingde Zhao, Sitao Luan, Ian Porada, Xiao-Wen Chang, Doina Precup

Temporal-Difference (TD) learning is a standard and very successful reinforcement learning approach, at the core of both algorithms that learn the value of a given policy, as well as algorithms which learn how to improve policies.

Meta-Learning

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