24 citations · 45 across the 9 of their papers we have counts for
9 papers
AutoContext: Instance-Level Context Learning for LLM Agents
Kuntai Cai, Juncheng Liu, Xianglin Yang +3
Current LLM agents typically lack instance-level context, which comprises concrete facts such as environment structure, system configurations, and local mechanics. Consequently, ex…
Node-wise Diffusion for Scalable Graph Learning
Keke Huang, Jing Tang, Juncheng Liu +2
Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis o…
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Juncheng Liu, Bryan Hooi, Kenji Kawaguchi +1
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses o…
Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysis
Yiwei Wang, Muhao Chen, Wenxuan Zhou +6
Recent literature focuses on utilizing the entity information in the sentence-level relation extraction (RE), but this risks leaking superficial and spurious clues of relations. As…
Dangling-Aware Entity Alignment with Mixed High-Order Proximities
Juncheng Liu, Zequn Sun, Bryan Hooi +5
We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of…
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Juncheng Liu, Kenji Kawaguchi, Bryan Hooi +2
Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. However, with their inherently finite aggregation layers, existing GNN mo…
A Fusion-Denoising Attack on InstaHide with Data Augmentation
Xinjian Luo, Xiaokui Xiao, Yuncheng Wu +2
InstaHide is a state-of-the-art mechanism for protecting private training images, by mixing multiple private images and modifying them such that their visual features are indisting…
LSCALE: Latent Space Clustering-Based Active Learning for Node Classification
Juncheng Liu, Yiwei Wang, Bryan Hooi +2
Node classification on graphs is an important task in many practical domains. It usually requires labels for training, which can be difficult or expensive to obtain in practice. Gi…
PANE: scalable and effective attributed network embedding
Renchi Yang, Jieming Shi, Xiaokui Xiao +3
Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node v in G to a compact vector Xv, which can be used in downst…