718 citations · 962 across the 19 of their papers we have counts for
16 papers · 1 filter
Intern-S2-Preview: Scientific Agentic Foundation Model
Lei Bai, Jiaqi Cao, Chiyu Chen +121
Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sus…
Efficient Link Prediction via GNN Layers Induced by Negative Sampling
Yuxin Wang, Xiannian Hu, Quan Gan +3
Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories. First, \emph{node-wise} architectures pre-compute individual embeddings for each…
From Hypergraph Energy Functions to Hypergraph Neural Networks
Yuxin Wang, Quan Gan, Xipeng Qiu +2
Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a v…
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
Kezhao Huang, Haitian Jiang, Minjie Wang +7
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU. Due to limited GPU memory, expensive d…
Refined Edge Usage of Graph Neural Networks for Edge Prediction
Jiarui Jin, Yangkun Wang, Weinan Zhang +5
Graph Neural Networks (GNNs), originally proposed for node classification, have also motivated many recent works on edge prediction (a.k.a., link prediction). However, existing met…
Learning Enhanced Representations for Tabular Data via Neighborhood Propagation
Kounianhua Du, Weinan Zhang, Ruiwen Zhou +6
Prediction over tabular data is an essential and fundamental problem in many important downstream tasks. However, existing methods either take a data instance of the table independ…
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Hongjoon Ahn, Yongyi Yang, Quan Gan +2
Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN…
CEP3: Community Event Prediction with Neural Point Process on Graph
Xuhong Wang, Sirui Chen, Yixuan He +4
Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represen…
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Tianyu Zhao, Cheng Yang, Yibo Li +7
Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due…
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Yixuan He, Quan Gan, David Wipf +3
Recovering global rankings from pairwise comparisons has wide applications from time synchronization to sports team ranking. Pairwise comparisons corresponding to matches in a comp…
Why Propagate Alone? Parallel Use of Labels and Features on Graphs
Yangkun Wang, Jiarui Jin, Weinan Zhang +7
Graph neural networks (GNNs) and label propagation represent two interrelated modeling strategies designed to exploit graph structure in tasks such as node property prediction. The…
TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting
Zonghan Wu, Da Zheng, Shirui Pan +3
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-t…
Graph Neural Networks Inspired by Classical Iterative Algorithms
Yongyi Yang, Tang Liu, Yangkun Wang +6
Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependenci…
A Biased Graph Neural Network Sampler with Near-Optimal Regret
Qingru Zhang, David Wipf, Quan Gan +1
Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data. However, given the increasing size of industria…
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Da Zheng, Chao Ma, Minjie Wang +6
Graph neural networks (GNN) have shown great success in learning from graph-structured data. They are widely used in various applications, such as recommendation, fraud detection,…
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye +12
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and im…