Timezone: »
Graph neural networks (GNNs), which propagate the node features through the edges and learn how to transform the aggregated features under label supervision, have achieved great success in supervised feature extraction for both node-level and graph-level classification tasks. However, GNNs typically treat the graph structure as given and ignore how the edges are formed. This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities, each of which contributes to the edges via a logical OR mechanism. Based on this generative model, we partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN-based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN-based predictor that combines community-specific GNNs for the end classification task. Extensive evaluations on real-world graph datasets have verified the effectiveness of the proposed method in learning discriminative representations for both node-level and graph-level classification tasks.
Author Information
Yilin He (The University of Texas at Austin)
Chaojie Wang (Nanyang Technological University)
Hao Zhang (Xidian University)
Bo Chen (Xidian University)
Mingyuan Zhou (University of Texas at Austin)
More from the Same Authors
-
2022 Poster: Knowledge-Aware Bayesian Deep Topic Model »
Dongsheng Wang · Yishi Xu · Miaoge Li · Zhibin Duan · Chaojie Wang · Bo Chen · Mingyuan Zhou -
2022 Poster: HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding »
Yishi Xu · Dongsheng Wang · Bo Chen · Ruiying Lu · Zhibin Duan · Mingyuan Zhou -
2022 : Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems »
Yihao Feng · Shentao Yang · Shujian Zhang · Jianguo Zhang · Caiming Xiong · Mingyuan Zhou · Huan Wang -
2022 : Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-Oriented Dialogue Systems »
Yihao Feng · Shentao Yang · Shujian Zhang · Jianguo Zhang · Caiming Xiong · Mingyuan Zhou · Huan Wang -
2022 : Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning »
Zhendong Wang · jonathan j hunt · Mingyuan Zhou -
2023 Poster: Beta Diffusion »
Mingyuan Zhou · Tianqi Chen · Huangjie Zheng · Zhendong Wang -
2023 Poster: Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models »
Zhendong Wang · Yifan Jiang · Huangjie Zheng · Peihao Wang · Pengcheng He · Zhangyang Wang · Weizhu Chen · Mingyuan Zhou -
2023 Poster: Few-shot Generation via Recalling the Episodic-Semantic Memory like Human Being »
Zhibin Duan · Zhiyi Lv · Chaojie Wang · Bo Chen · Bo An · Mingyuan Zhou -
2023 Poster: Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection »
Ruiying Lu · YuJie Wu · Long Tian · Dongsheng Wang · Bo Chen · Xiyang Liu · Ruimin Hu -
2023 Poster: Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes »
Yishi Xu · Jianqiao Sun · Yudi Su · Xinyang Liu · Zhibin Duan · Bo Chen · Mingyuan Zhou -
2023 Poster: Tuning Multi-mode Token-level Prompt Alignment across Modalities »
Dongsheng Wang · Miaoge Li · Xinyang Liu · MingSheng Xu · Bo Chen · Hanwang Zhang -
2023 Poster: Preference-grounded Token-level Guidance for Language Model Fine-tuning »
Shentao Yang · Shujian Zhang · Congying Xia · Yihao Feng · Caiming Xiong · Mingyuan Zhou -
2023 Poster: In-Context Learning Unlocked for Diffusion Models »
Zhendong Wang · Yifan Jiang · Yadong Lu · yelong shen · Pengcheng He · Weizhu Chen · Zhangyang Wang · Mingyuan Zhou -
2022 Spotlight: Lightning Talks 5B-4 »
Yuezhi Yang · Zeyu Yang · Yong Lin · Yishi Xu · Linan Yue · Tao Yang · Weixin Chen · Qi Liu · Jiaqi Chen · Dongsheng Wang · Baoyuan Wu · Yuwang Wang · Hao Pan · Shengyu Zhu · Zhenwei Miao · Yan Lu · Lu Tan · Bo Chen · Yichao Du · Haoqian Wang · Wei Li · Yanqing An · Ruiying Lu · Peng Cui · Nanning Zheng · Li Wang · Zhibin Duan · Xiatian Zhu · Mingyuan Zhou · Enhong Chen · Li Zhang -
2022 Spotlight: HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding »
Yishi Xu · Dongsheng Wang · Bo Chen · Ruiying Lu · Zhibin Duan · Mingyuan Zhou -
2022 Spotlight: Lightning Talks 2A-4 »
Sarthak Mittal · Richard Grumitt · Zuoyu Yan · Lihao Wang · Dongsheng Wang · Alexander Korotin · Jiangxin Sun · Ankit Gupta · Vage Egiazarian · Tengfei Ma · Yi Zhou · Yishi Xu · Albert Gu · Biwei Dai · Chunyu Wang · Yoshua Bengio · Uros Seljak · Miaoge Li · Guillaume Lajoie · Yiqun Wang · Liangcai Gao · Lingxiao Li · Jonathan Berant · Huang Hu · Xiaoqing Zheng · Zhibin Duan · Hanjiang Lai · Evgeny Burnaev · Zhi Tang · Zhi Jin · Xuanjing Huang · Chaojie Wang · Yusu Wang · Jian-Fang Hu · Bo Chen · Chao Chen · Hao Zhou · Mingyuan Zhou -
2022 Spotlight: Knowledge-Aware Bayesian Deep Topic Model »
Dongsheng Wang · Yishi Xu · Miaoge Li · Zhibin Duan · Chaojie Wang · Bo Chen · Mingyuan Zhou -
2022 Poster: Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification »
Dandan Guo · Zhuo Li · meixi zheng · He Zhao · Mingyuan Zhou · Hongyuan Zha -
2022 Poster: Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport »
Dandan Guo · Long Tian · He Zhao · Mingyuan Zhou · Hongyuan Zha -
2022 Poster: Alleviating "Posterior Collapse'' in Deep Topic Models via Policy Gradient »
Yewen Li · Chaojie Wang · Zhibin Duan · Dongsheng Wang · Bo Chen · Bo An · Mingyuan Zhou -
2022 Poster: Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE »
Yewen Li · Chaojie Wang · Xiaobo Xia · Tongliang Liu · xin miao · Bo An -
2022 Poster: A Unified Framework for Alternating Offline Model Training and Policy Learning »
Shentao Yang · Shujian Zhang · Yihao Feng · Mingyuan Zhou -
2022 Poster: CARD: Classification and Regression Diffusion Models »
Xizewen Han · Huangjie Zheng · Mingyuan Zhou -
2021 Poster: Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions »
Huangjie Zheng · Mingyuan Zhou -
2021 Poster: Alignment Attention by Matching Key and Query Distributions »
Shujian Zhang · Xinjie Fan · Huangjie Zheng · Korawat Tanwisuth · Mingyuan Zhou -
2021 Poster: Probabilistic Margins for Instance Reweighting in Adversarial Training »
qizhou wang · Feng Liu · Bo Han · Tongliang Liu · Chen Gong · Gang Niu · Mingyuan Zhou · Masashi Sugiyama -
2021 Poster: Convex Polytope Trees »
Mohammadreza Armandpour · Ali Sadeghian · Mingyuan Zhou -
2021 Poster: TopicNet: Semantic Graph-Guided Topic Discovery »
Zhibin Duan · Yishi Xu · Bo Chen · Dongsheng Wang · Chaojie Wang · Mingyuan Zhou -
2021 Poster: A Prototype-Oriented Framework for Unsupervised Domain Adaptation »
Korawat Tanwisuth · Xinjie Fan · Huangjie Zheng · Shujian Zhang · Hao Zhang · Bo Chen · Mingyuan Zhou -
2021 Poster: CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator »
Alek Dimitriev · Mingyuan Zhou -
2020 Poster: Bidirectional Convolutional Poisson Gamma Dynamical Systems »
wenchao chen · Chaojie Wang · Bo Chen · Yicheng Liu · Hao Zhang · Mingyuan Zhou -
2020 Poster: Implicit Distributional Reinforcement Learning »
Yuguang Yue · Zhendong Wang · Mingyuan Zhou -
2020 Poster: Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network »
Chaojie Wang · Hao Zhang · Bo Chen · Dongsheng Wang · Zhengjue Wang · Mingyuan Zhou -
2020 Poster: Bayesian Attention Modules »
Xinjie Fan · Shujian Zhang · Bo Chen · Mingyuan Zhou -
2019 Poster: Variational Graph Recurrent Neural Networks »
Ehsan Hajiramezanali · Arman Hasanzadeh · Krishna Narayanan · Nick Duffield · Mingyuan Zhou · Xiaoning Qian -
2019 Poster: Semi-Implicit Graph Variational Auto-Encoders »
Arman Hasanzadeh · Ehsan Hajiramezanali · Krishna Narayanan · Nick Duffield · Mingyuan Zhou · Xiaoning Qian -
2019 Poster: Poisson-Randomized Gamma Dynamical Systems »
Aaron Schein · Scott Linderman · Mingyuan Zhou · David Blei · Hanna Wallach -
2018 Poster: Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks »
Quan Zhang · Mingyuan Zhou -
2018 Poster: Deep Poisson gamma dynamical systems »
Dandan Guo · Bo Chen · Hao Zhang · Mingyuan Zhou -
2018 Poster: Dirichlet belief networks for topic structure learning »
He Zhao · Lan Du · Wray Buntine · Mingyuan Zhou -
2018 Poster: Parsimonious Bayesian deep networks »
Mingyuan Zhou -
2018 Poster: Masking: A New Perspective of Noisy Supervision »
Bo Han · Jiangchao Yao · Gang Niu · Mingyuan Zhou · Ivor Tsang · Ya Zhang · Masashi Sugiyama -
2018 Poster: Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data »
Ehsan Hajiramezanali · Siamak Zamani Dadaneh · Alireza Karbalayghareh · Mingyuan Zhou · Xiaoning Qian -
2016 Poster: Poisson-Gamma dynamical systems »
Aaron Schein · Hanna Wallach · Mingyuan Zhou -
2016 Oral: Poisson-Gamma dynamical systems »
Aaron Schein · Hanna Wallach · Mingyuan Zhou -
2015 Poster: The Poisson Gamma Belief Network »
Mingyuan Zhou · Yulai Cong · Bo Chen -
2014 Poster: Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling »
Mingyuan Zhou -
2012 Poster: Augment-and-Conquer Negative Binomial Processes »
Mingyuan Zhou · Lawrence Carin -
2012 Spotlight: Augment-and-Conquer Negative Binomial Processes »
Mingyuan Zhou · Lawrence Carin -
2011 Poster: On the Analysis of Multi-Channel Neural Spike Data »
Bo Chen · David Carlson · Lawrence Carin -
2009 Poster: Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations »
Mingyuan Zhou · Haojun Chen · John Paisley · Lu Ren · Guillermo Sapiro · Lawrence Carin -
2009 Oral: Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations »
Mingyuan Zhou · Haojun Chen · John Paisley · Lu Ren · Guillermo Sapiro · Larry Carin