11 citations · 20 across the 13 of their papers we have counts for
13 papers
Scalable Optimal Transport Algorithm for Network Alignment
Elaheh Hassani, Durga Mandarapu, Qi Yu +2
Network alignment identifies node correspondences across different networks and is a fundamental primitive in many data science applications, including social network analysis, fra…
PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment
Qi Yu, Zhichen Zeng, Yuchen Yan +5
Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. D…
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics
Zhichen Zeng, Ruizhong Qiu, Wenxuan Bao +6
Graph neural networks, despite their impressive performance, are highly vulnerable to distribution shifts on graphs. Existing graph domain adaptation (graph DA) methods often impli…
CATS: Mitigating Correlation Shift for Multivariate Time Series Classification
Xiao Lin, Zhichen Zeng, Tianxin Wei +3
Unsupervised Domain Adaptation (UDA) leverages labeled source data to train models for unlabeled target data. Given the prevalence of multivariate time series (MTS) data across var…
Taming Knowledge Conflicts in Language Models
Gaotang Li, Yuzhong Chen, Hanghang Tong
Language Models (LMs) often encounter knowledge conflicts when parametric memory contradicts contextual knowledge. Previous works attribute this conflict to the interplay between "…
Joint Optimal Transport and Embedding for Network Alignment
Qi Yu, Zhichen Zeng, Yuchen Yan +3
Network alignment, which aims to find node correspondence across different networks, is the cornerstone of various downstream multi-network and Web mining tasks. Most of the embedd…
THeGCN: Temporal Heterophilic Graph Convolutional Network
Yuchen Yan, Yuzhong Chen, Huiyuan Chen +6
Graph Neural Networks (GNNs) have exhibited remarkable efficacy in diverse graph learning tasks, particularly on static homophilic graphs. Recent attention has pivoted towards more…
A Collaborative Ensemble Framework for CTR Prediction
Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu +13
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into…
F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data
Zexing Xu, Linjun Zhang, Sitan Yang +4
Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data f…
SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering
Xiaodong Yang, Huiyuan Chen, Yuchen Yan +5
The learning objective is integral to collaborative filtering systems, where the Bayesian Personalized Ranking (BPR) loss is widely used for learning informative backbones. However…
Masked Graph Transformer for Large-Scale Recommendation
Huiyuan Chen, Zhe Xu, Chin-Chia Michael Yeh +4
Graph Transformers have garnered significant attention for learning graph-structured data, thanks to their superb ability to capture long-range dependencies among nodes. However, t…
Discrete-state Continuous-time Diffusion for Graph Generation
Zhe Xu, Ruizhong Qiu, Yuzhong Chen +6
Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design. Diffusion generative models, as an emerging research…
Fine-grained Graph Rationalization
Zhe Xu, Menghai Pan, Yuzhong Chen +4
Rationale discovery is defined as finding a subset of the input data that maximally supports the prediction of downstream tasks. In the context of graph machine learning, graph rat…