7 citations · 7 across the 7 of their papers we have counts for
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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…