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no code implementations • CCL 2021 • Shangyi Ning, Guanying Li, Qin Chen, Zengfeng Huang, Baohua Zhou, Zhongyu Wei

“用户建模已经引起了学术界和工业界的广泛关注。现有的方法大多侧重于将用户间的人际关系融入社区, 而用户生成的内容(如帖子)却没有得到很好的研究。在本文中, 我们通过实际舆情传播相关的分析表明, 在舆情传播过程中对用户属性进行研究的重要作用, 并且提出了用户资料数据的筛选方法。同时, 我们提出了一种通过异构多质心图池为用户捕获更多不同社区特征的建模。我们首先构造了一个由用户和关键字组成的异质图, 并在其上采用了一个异质图神经网络。为了方便用户建模的图表示, 提出了一种多质心图池化机制, 将多质心的集群特征融入到表示学习中。在三个基准数据集上的大量实验表明了该方法的有效性。”

1 code implementation • EMNLP 2020 • Siyuan Wang, Zhongyu Wei, Zhihao Fan, Zengfeng Huang, Weijian Sun, Qi Zhang, Xuanjing Huang

Human evaluation also proves that our model is able to generate relevant and informative questions.

no code implementations • 18 Mar 2023 • Liang Yan, Shengzhong Zhang, Bisheng Li, Min Zhou, Zengfeng Huang

To select which unlabeled nodes to add, we propose geometric ranking to rank unlabeled nodes.

no code implementations • 18 Jan 2023 • Kezhao Huang, Haitian Jiang, Minjie Wang, Guangxuan Xiao, David Wipf, Xiang Song, Quan Gan, Zengfeng Huang, Jidong Zhai, Zheng Zhang

A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU.

no code implementations • 3 Oct 2022 • Zepeng Zhang, Songtao Lu, Zengfeng Huang, Ziping Zhao

In this work, we propose a novel interpretable message passing scheme with adaptive structure (ASMP) to defend against adversarial attacks on graph structure.

no code implementations • 12 Jul 2022 • Jinghui Xia, Zengfeng Huang

In particular, a new polynomial time algorithm with $O(\frac{n(k+\log n)}{\delta^2} + \text{poly}(k,\frac{1}{\delta}, \log n))$ queries is proposed.

1 code implementation • 27 May 2022 • Yongyi Yang, Zengfeng Huang, David Wipf

Deep learning models such as the Transformer are often constructed by heuristics and experience.

1 code implementation • 18 Apr 2022 • Bisheng Li, Min Zhou, Shengzhong Zhang, Menglin Yang, Defu Lian, Zengfeng Huang

Regarding missing link inference of diverse networks, we revisit the link prediction techniques and identify the importance of both the structural and attribute information.

no code implementations • 19 Mar 2022 • Jie Chen, Shouzhen Chen, Junbin Gao, Zengfeng Huang, Junping Zhang, Jian Pu

Moreover, we propose a Conv-Agnostic GNNs framework (CAGNNs) to enhance the performance of GNNs on heterophily datasets by learning the neighbor effect for each node.

no code implementations • 12 Nov 2021 • Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf

It has been observed that graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient modeling long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations or sensitivity to spurious edges.

no code implementations • 19 Oct 2021 • Yasong Feng, Zengfeng Huang, Tianyu Wang

Specifically, we show that for a $T$-step problem with Lipschitz reward of zooming dimension $d_z$, our algorithm achieves theoretically optimal regret rate $\widetilde{\mathcal{O}}\left(T^{\frac{d_z+1}{d_z+2}}\right)$ using only $ \mathcal{O} \left( \log\log T\right) $ batches.

no code implementations • ICLR 2022 • Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf

In this regard, it has recently been proposed to use a randomly-selected portion of the training labels as GNN inputs, concatenated with the original node features for making predictions on the remaining labels.

no code implementations • 2 Oct 2021 • Zhengpin Li, Zheng Wei, Zengfeng Huang, Xiaojun Mao, Jian Wang

In this paper, we propose a unified framework for ensuring a strong privacy guarantee of one-bit matrix completion with DP.

no code implementations • 29 Sep 2021 • Ziang Zhou, Jieming Shi, Shengzhong Zhang, Zengfeng Huang, Qing Li

Therefore, we propose an effective framework, Stabilized self-training with Negative sampling (SN), which is applicable to existing GNNs to stabilize the training process and enhance the training data, and consequently, boost classification accuracy on graphs with few labeled data.

1 code implementation • 8 Jul 2021 • Menglin Yang, Min Zhou, Marcus Kalander, Zengfeng Huang, Irwin King

To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry.

1 code implementation • NeurIPS 2021 • Mingguo He, Zhewei Wei, Zengfeng Huang, Hongteng Xu

Many representative graph neural networks, e. g., GPR-GNN and ChebNet, approximate graph convolutions with graph spectral filters.

GPR Node Classification on Non-Homophilic (Heterophilic) Graphs

1 code implementation • 10 Jun 2021 • Xingyi Zhang, Kun Xie, Sibo Wang, Zengfeng Huang

Recent progress on node embedding shows that proximity matrix factorization methods gain superb performance and scale to large graphs with millions of nodes.

1 code implementation • 9 Jun 2021 • Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, Min Zhou

Scalability of graph neural networks remains one of the major challenges in graph machine learning.

no code implementations • NeurIPS 2021 • Houshuang Chen, Zengfeng Huang, Shuai Li, Chihao Zhang

We propose the notions of the fractional weak domination number $\delta^*$ and the $k$-packing independence number capturing upper bound and lower bound for the regret respectively.

1 code implementation • 10 Mar 2021 • Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf

Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.

no code implementations • 3 Dec 2020 • Zhongzheng Xiong, Jialin Sun, Xiaojun Mao, Jian Wang, Shan Ying, Zengfeng Huang

In this paper, we consider the problem of discrete distribution estimation under local differential privacy constraints.

no code implementations • Findings of the Association for Computational Linguistics 2020 • Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang

Terms contained in Gene Ontology (GO) have been widely used in biology and bio-medicine.

3 code implementations • ICML 2020 • Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li

We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.

Node Classification on Non-Homophilic (Heterophilic) Graphs Node Property Prediction

1 code implementation • 30 Jun 2020 • Shengzhong Zhang, Zengfeng Huang, Haicang Zhou, Ziang Zhou

A key of success to such contrastive learning methods is how to draw positive and negative samples.

1 code implementation • NeurIPS 2019 • Zengfeng Huang, Ziyue Huang, Yilei Wang, Ke Yi

We consider the problem of estimating the mean of a set of vectors, which are stored in a distributed system.

no code implementations • 11 Nov 2019 • Songtao Liu, Lingwei Chen, Hanze Dong, ZiHao Wang, Dinghao Wu, Zengfeng Huang

Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.

1 code implementation • 7 Oct 2019 • Ziang Zhou, Jieming Shi, Shengzhong Zhang, Zengfeng Huang, Qing Li

However, under extreme cases when very few labels are available (e. g., 1 labeled node per class), GNNs suffer from severe performance degradation.

no code implementations • ICLR 2019 • Shengzhong Zhang, Ziang Zhou, Zengfeng Huang, Zhongyu Wei

We consider the fundamental problem of semi-supervised node classification in attributed graphs with a focus on \emph{few-shot} learning.

no code implementations • ICML 2018 • Zengfeng Huang

In particular, we provide new space-optimal algorithms with faster running times.

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