publications

2025

1. KDD

   Graph odes and beyond: A comprehensive survey on integrating differential equations with graph neural networks

   Zewen Liu^*, Xiaoda Wang^*, Bohan Wang, Zijie Huang, Carl Yang, and Wei Jin

   In Proceedings of the 31th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025

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   Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as powerful tools for learning on graph-structured data, while differential equations provide a principled framework for modeling continuous dynamics across time and space. The intersection of these fields has led to innovative approaches that leverage the strengths of both, enabling applications in physics-informed learning, spatiotemporal modeling, and scientific computing. This survey aims to provide a comprehensive overview of the burgeoning research at the intersection of GNNs and DEs. We will categorize existing methods, discuss their underlying principles, and highlight their applications across domains such as molecular modeling, traffic prediction, and epidemic spreading. Furthermore, we identify open challenges and outline future research directions to advance this interdisciplinary field.

2024

1. arXiv

   Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation

   Bohan Wang, Yurui Chang, and Lu Lin

   arXiv preprint arXiv:2410.17506, 2024

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   Distribution shifts between training and testing datasets significantly impair the model performance on graph learning. A commonly-taken causal view in graph invariant learning suggests that stable predictive features of graphs are causally associated with labels, whereas varying environmental features lead to distribution shifts. In particular, covariate shifts caused by unseen environments in test graphs underscore the critical need for out-of-distribution (OOD) generalization. Existing graph augmentation methods designed to address the covariate shift often disentangle the stable and environmental features in the input space, and selectively perturb or mixup the environmental features. However, such perturbation-based methods heavily rely on an accurate separation of stable and environmental features, and their exploration ability is confined to existing environmental features in the training distribution. To overcome these limitations, we introduce a novel approach using score-based graph generation strategies that synthesize unseen environmental features while preserving the validity and stable features of overall graph patterns. Our comprehensive empirical evaluations demonstrate the enhanced effectiveness of our method in improving graph OOD generalization.

2023

1. ICLR

   DiGress: Discrete Denoising diffusion for graph generation

   Clement Vignac^*, Igor Krawczuk^*, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard

   In The Eleventh International Conference on Learning Representations, 2023

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   This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes. Our model utilizes a discrete diffusion process that progressively edits graphs with noise, through the process of adding or removing edges and changing the categories. A graph transformer network is trained to revert this process, simplifying the problem of distribution learning over graphs into a sequence of node and edge classification tasks. We further improve sample quality by introducing a Markovian noise model that preserves the marginal distribution of node and edge types during diffusion, and by incorporating auxiliary graph-theoretic features. A procedure for conditioning the generation on graph-level features is also proposed. DiGress achieves state-of-the-art performance on molecular and non-molecular datasets, with up to 3x validity improvement on a planar graph dataset. It is also the first model to scale to the large GuacaMol dataset containing 1.3M drug-like molecules without the use of molecule-specific representations.

2. CVPR

   Regularization of polynomial networks for image recognition

   Grigorios G Chrysos, Bohan Wang, Jiankang Deng, and Volkan Cevher

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

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   Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising performance and improved interpretability but have yet to reach the performance of the powerful DNN baselines. In this work, we aim to close this performance gap. We introduce a class of PNs, which are able to reach the performance of ResNet across a range of six benchmarks. We demonstrate that strong regularization is critical and conduct an extensive study of the exact regularization schemes required to match performance. To further motivate the regularization schemes, we introduce D-PolyNets that achieve a higher-degree of expansion than previously proposed polynomial networks. D-PolyNets are more parameter-efficient while achieving a similar performance as other polynomial networks. We expect that our new models can lead to an understanding of the role of elementwise activation functions (which are no longer required for training PNs).

3. INTERSPEECH

   ALO-VC: Any-to-any Low-latency One-shot Voice Conversion

   Bohan Wang, Damien Ronssin, and Milos Cernak

   In Interspeech 2023, 2023

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   This paper presents ALO-VC, a non-parallel low-latency one-shot phonetic posteriorgrams (PPGs) based voice conversion method. ALO-VC enables any-to-any voice conversion using only one utterance from the target speaker, with only 47.5 ms future look-ahead. The proposed hybrid signal processing and machine learning pipeline combines a pre-trained speaker encoder, a pitch predictor to predict the converted speech’s prosody, and positional encoding to convey the phoneme’s location information. We introduce two system versions: ALO-VC-R, which uses a pre-trained d-vector speaker encoder, and ALO-VC-E, which improves performance using the ECAPA-TDNN speaker encoder. The experimental results demonstrate both ALO-VC-R and ALO-VC-E can achieve comparable performance to non-causal baseline systems on the VCTK dataset and two out-of-domain datasets. Furthermore, both proposed systems can be deployed on a single CPU core with 55 ms latency and 0.78 real-time factor.

2022

1. SwissText

   Detection of Typical Sentence Errors in Speech Recognition Output.

   Bohan Wang^*, Ke Wang^*, Siran Li^*, and Mark Cieliebak

   In SwissText, 2022

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   This paper presents a deep learning based model to detect the completeness and correctness of a sentence. It’s designed specifically for detecting errors in speech recognition systems and takes several typical recognition errors into account, including false sentence boundary, missing words, repeating words and false word recognition. The model can be applied to evaluate the quality of the recognized transcripts, and the optimal model reports over 90.5% accuracy on detecting whether the system completely and correctly recognizes a sentence.