23 citations · 33 across the 15 of their papers we have counts for
15 papers
Weisfeiler Lehman Test on Combinatorial Complexes: Generalized Expressive Power of Topological Neural Networks
Jiawen Chen, Qi Shao, Zhiqiang Ge +2
Topological neural networks have emerged as effective tools for modeling higher-order relational structures beyond pairwise graphs, including hypergraphs, simplicial complexes, and…
From Uniform to Learned Graph Priors: Diffusion for Structure Discovery
Qi Shao, Hao Guo, Jiawen Chen +2
Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges. However, these methods typically…
Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations
Qi Shao, Duxin Chen, Jiawen Chen +5
Predicting the behavior of ultra-large complex systems, from climate to biological and technological networks, is a central unsolved challenge. Existing approaches face a fundament…
TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction
Hantong Feng, Yonggang Wu, Duxin Chen +1
Dynamic link prediction plays a crucial role in diverse applications including social network analysis, communication forecasting, and financial modeling. While recent Transformer-…
CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
Huiyang Yi, Xiaojian Shen, Yonggang Wu +3
Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on untestable causal assumptions and by the…
CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks
Hantong Feng, Duxin Chen, Wenwu Yu
Temporal link prediction is crucial for rapidly growing social networks. Existing methods often overlook the underlying causal mechanisms that drive link formation, making it diffi…
CCMamba: Topologically-Informed Selective State-Space Networks on Combinatorial Complexes for Higher-Order Graph Learning
Jiawen Chen, Qi Shao, Mingtong Zhou +2
Topological deep learning has emerged as a powerful paradigm for modeling higher-order relational structures beyond pairwise interactions that standard graph neural networks fail t…
DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs
Jiawen Chen, Yanyan He, Qi Shao +4
Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems. However, most existing meth…
The Robustness of Differentiable Causal Discovery in Misspecified Scenarios
Huiyang Yi, Yanyan He, Duxin Chen +3
Causal discovery aims to learn causal relationships between variables from targeted data, making it a fundamental task in machine learning. However, causal discovery algorithms oft…
Critical Nodes Identification in Complex Networks: A Survey
Duxin Chen, Jiawen Chen, Xiaoyu Zhang +5
Complex networks have become essential tools for understanding diverse phenomena in social systems, traffic systems, biomolecular systems, and financial systems. Identifying critic…
Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs
Jiawen Chen, Qi Shao, Duxin Chen +1
Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often strugg…
MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction
Shuang Li, Ziyuan Pu, Nan Zhang +4
Traffic crashes profoundly impede traffic efficiency and pose economic challenges. Accurate prediction of post-crash traffic status provides essential information for evaluating tr…
Spatio-Temporal Graphical Counterfactuals: An Overview
Mingyu Kang, Duxin Chen, Ziyuan Pu +2
Counterfactual thinking is a crucial yet challenging topic for artificial intelligence to learn knowledge from data and ultimately improve performance for new scenarios. Many resea…
Interpretable System Identification and Long-term Prediction on Time-Series Data
Xiaoyi Liu, Duxin Chen, Wenjia Wei +2
Time-series prediction has drawn considerable attention during the past decades fueled by the emerging advances of deep learning methods. However, most neural network based methods…
Identifying Unique Spatial-Temporal Bayesian Network without Markov Equivalence
Mingyu Kang, Duxin Chen, Ning Meng +2
Identifying vanilla Bayesian network to model spatial-temporal causality can be a critical yet challenging task. Different Markovian-equivalent directed acyclic graphs would be ide…