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Found 17 papers, 7 papers with code
TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
Recently, Temporal Graph Neural Networks (TGNNs) have demonstrated state-of-the-art performance in various high-impact applications, including fraud detection and content recommendation.
Mixture of Weak & Strong Experts on Graphs
We propose to decouple the two modalities by mixture of weak and strong experts (Mowst), where the weak expert is a light-weight Multi-layer Perceptron (MLP), and the strong expert is an off-the-shelf Graph Neural Network (GNN).
Deceptive Fairness Attacks on Graphs via Meta Learning
We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively?
Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models
Breakdown analysis further highlights RESPROMPT particularly excels in complex multi-step reasoning: for questions demanding at least five reasoning steps, RESPROMPT outperforms the best CoT based benchmarks by a remarkable average improvement of 21. 1% on LLaMA-65B and 14. 3% on LLaMA2-70B.
Hierarchical Multi-Marginal Optimal Transport for Network Alignment
To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly.
User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations
Acknowledging the recent advancements in explainable recommender systems that enhance users' understanding of recommendation mechanisms, we propose leveraging these advancements to improve user controllability.
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics.
Explainable Fairness in Recommendation
In this paper, we study the problem of explainable fairness, which helps to gain insights about why a system is fair or unfair, and guides the design of fair recommender systems with a more informed and unified methodology.
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
Decoupling the Depth and Scope of Graph Neural Networks
We propose a design principle to decouple the depth and scope of GNNs -- to generate representation of a target entity (i. e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph.
Ranked #3 on
Node Classification
on Reddit
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability
To achieve efficient and scalable training, we propose temporal-union graph structure and its associated subgraph-based node sampling strategy.
Deep Graph Neural Networks with Shallow Subgraph Samplers
We propose a simple "deep GNN, shallow sampler" design principle to improve both the GNN accuracy and efficiency -- to generate representation of a target node, we use a deep GNN to pass messages only within a shallow, localized subgraph.
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link).
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Revisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years.
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
In particular, RGE is shown to achieve \emph{(quasi-)linear scalability} with respect to the number and the size of the graphs.
From Node Embedding to Graph Embedding: Scalable Global Graph Kernel via Random Features
Graph kernels are one of the most important methods for graph data analysis and have been successfully applied in diverse applications.
Scalable Spectral Clustering Using Random Binning Features
Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.
Ranked #5 on
Image/Document Clustering
on pendigits
