出版

2024

1. ReiPool: Reinforced Pooling Graph Neural Networks for Graph-Level Representation Learning

   Xuexiong Luo , Sheng Zhang , Jia Wu , Hongyang Chen , Hao Peng , Chuan Zhou , Zhao Li , and 2 more authors

   IEEE Transactions on Knowledge and Data Engineering, 2024

   Abs

   Current graph pooling methods easily destroy the global structure of the input graph during the graph pooling process and mechanically control the number of graph pooling layers for different datasets. We analyze that each graph in the graph dataset has different structures and sizes, so a specfic graph pooling strategy needs to be developed. Thus, this work aims to introduce multi-agent reinforcement learning to guide the graph pooling process to generate the most representative coarsened graph for each graph. Specifically, we design a hybrid graph coarsening module to coarsen first-order and star structures of nodes, enabling to preserve the global structure of the graph. Then, an adaptive graph pooling module with multi-agent RL is proposed to generate the most representative coarsened graph for each graph, where the feature-agent controls the fusion of coarsened node features and the depth-agent controls the number of graph coarsening layers. Finally, we design a graph-level contrast between the coarsened graph and the input graph to enhance global information preservation for graph-level representations.

2. Exploring Robustness of GNN against Universal Injection Attack from a Worst-case Perspective

   Dandan Ni , Sheng Zhang , Cong Deng , Han Liu , Gang Chen , Minhao Cheng , and Hongyang Chen.

   In , 2024
3. Unsupervised Heterogeneous Graph Rewriting Attack via Node Clustering

   ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2024
4. Geometric-Facilitated Denoising Diffusion Model for 3D Molecule Generation

   Can Xu , Haosen Wang , Weigang Wang , Pengfei Zheng , and Hongyang Chen

   Proceedings of the AAAI Conference on Artificial Intelligence, Mar 2024

   Abs

   Denoising diffusion models have shown great potential in multiple research areas. Existing diffusion-based generative methods on de novo 3D molecule generation face two major challenges. Since majority heavy atoms in molecules allow connections to multiple atoms through single bonds, solely using pair-wise distance to model molecule geometries is insufficient. Therefore, the first one involves proposing an effective neural network as the denoising kernel that is capable to capture complex multi-body interatomic relationships and learn high-quality features. Due to the discrete nature of graphs, mainstream diffusion-based methods for molecules heavily rely on predefined rules and generate edges in an indirect manner. The second challenge involves accommodating molecule generation to diffusion and accurately predicting the existence of bonds. In our research, we view the iterative way of updating molecule conformations in diffusion process is consistent with molecular dynamics and introduce a novel molecule generation method named Geometric-Facilitated Molecular Diffusion (GFMDiff). For the first challenge, we introduce a Dual-track Transformer Network (DTN) to fully excevate global spatial relationships and learn high quality representations which contribute to accurate predictions of features and geometries. As for the second challenge, we design Geometric-facilitated Loss (GFLoss) which intervenes the formation of bonds during the training period, instead of directly embedding edges into the latent space. Comprehensive experiments on current benchmarks demonstrate the superiority of GFMDiff.

5. Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation

   Zhen Zhang , Meihan Liu , Anhui Wang , Hongyang Chen , Zhao Li , Jiajun Bu , and Bingsheng He

   arXiv preprint arXiv:2403.01467, Mar 2024

2023

1. Mixed-Order Relation-Aware Recurrent Neural Networks for Spatio-Temporal Forecasting

   Yuxuan Liang , Kun Ouyang , Yiwei Wang , Zheyi Pan , Yifang Yin , Hongyang Chen , Junbo Zhang , and 3 more authors

   IEEE Transactions on Knowledge and Data Engineering, Mar 2023
2. SSPAttack: a simple and sweet paradigm for black-box hard-label textual adversarial attack

   Han Liu , Zhi Xu , Xiaotong Zhang , Xiaoming Xu , Feng Zhang , Fenglong Ma , Hongyang Chen , and 2 more authors

   In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence , Mar 2023

   Abs

   Hard-label textual adversarial attack is a challenging task, as only the predicted label information is available, and the text space is discrete and non-differentiable. Relevant research work is still in fancy and just a handful of methods are proposed. However, existing methods suffer from either the high complexity of genetic algorithms or inaccurate gradient estimation, thus are arduous to obtain adversarial examples with high semantic similarity and low perturbation rate under the tight-budget scenario. In this paper, we propose a simple and sweet paradigm for hard-label textual adversarial attack, named SSPAttack. Specifically, SSPAttack first utilizes initialization to generate an adversarial example, and removes unnecessary replacement words to reduce the number of changed words. Then it determines the replacement order and searches for an anchor synonym, thus avoiding going through all the synonyms. Finally, it pushes substitution words towards original words until an appropriate adversarial example is obtained. The core idea of SSPAttack is just swapping words whose mechanism is simple. Experimental results on eight benchmark datasets and two real-world APIs have shown that the performance of SSPAttack is sweet in terms of similarity, perturbation rate and query efficiency.

3. Boosting few-shot text classification via distribution estimation

   Han Liu , Feng Zhang , Xiaotong Zhang , Siyang Zhao , Fenglong Ma , Xiao-Ming Wu , Hongyang Chen , and 2 more authors

   In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence , Mar 2023

   Abs

   Distribution estimation has been demonstrated as one of the most effective approaches in dealing with few-shot image classification, as the low-level patterns and underlying representations can be easily transferred across different tasks in computer vision domain. However, directly applying this approach to few-shot text classification is challenging, since leveraging the statistics of known classes with sufficient samples to calibrate the distributions of novel classes may cause negative effects due to serious category difference in text domain. To alleviate this issue, we propose two simple yet effective strategies to estimate the distributions of the novel classes by utilizing unlabeled query samples, thus avoiding the potential negative transfer issue. Specifically, we first assume a class or sample follows the Gaussian distribution, and use the original support set and the nearest few query samples to estimate the corresponding mean and covariance. Then, we augment the labeled samples by sampling from the estimated distribution, which can provide sufficient supervision for training the classification model. Extensive experiments on eight few-shot text classification datasets show that the proposed method outperforms state-of-the-art baselines significantly.

4. Efficient Resistance Distance Computation: The Power of Landmark-based Approaches

   Meihao Liao , Rong-Hua Li , Qiangqiang Dai , Hongyang Chen , Hongchao Qin , and Guoren Wang

   Proc. ACM Manag. Data, May 2023

   Abs

   Resistance distance is a fundamental metric to measure the similarity between two nodes in graphs which has been widely used in many real-world applications. In this paper, we study two problems on approximately computing resistance distance: (i) single-pair query which aims at calculating the resistance distance r(s, t) for a given pair of nodes (s, t); and (ii) single-source query which is to compute all the resistance distances r(s, u) for all nodes u in the graph with a given source node s. Existing algorithms for these two resistance distance query problems are often costly on large graphs. To efficiently solve these problems, we first establish several interesting connections among resistance distance, a new concept called v-absorbed random walk, random spanning forests, and a newly-developed v-absorbed push procedure. Based on such new connections, we propose three novel and efficient sampling-based algorithms as well as a deterministic algorithm for single-pair query; and we develop an online and two index-based approximation algorithms for single-source query. We show that the two index-based algorithms for single-source query take almost the same running time as the algorithms for single-pair query with the aid of a linear-size index. The striking feature of all our algorithms is that they are allowed to select an easy-to-hit node by random walks on the graph. Such an easy-to-hit landmark node v can make the v-absorbed random walk sampling, spanning tree sampling, as well as the v-absorbed push more efficient, thus significantly improving the performance of our algorithms. Extensive experiments on 5 real-life datasets show that our algorithms substantially outperform the state-of-the-art algorithms for two resistance distance query problems in terms of both running time and estimation errors.

2022

1. TrajFormer: Efficient Trajectory Classification with Transformers

   Yuxuan Liang , Kun Ouyang , Yiwei Wang , Xu Liu , Hongyang Chen , Junbo Zhang , Yu Zheng , and 1 more author

   In Proceedings of the 31st ACM International Conference on Information & Knowledge Management , May 2022

   Abs

   Transformers have been an efficient alternative to recurrent neural networks in many sequential learning tasks. When adapting transformers to modeling trajectories, we encounter two major issues. First, being originally designed for language modeling, transformers assume regular intervals between input tokens, which contradicts the irregularity of trajectories. Second, transformers often suffer high computational costs, especially for long trajectories. In this paper, we address these challenges by presenting a novel transformer architecture entitled TrajFormer. Our model first generates continuous point embeddings by jointly considering the input features and the information of spatio-temporal intervals, and then adopts a squeeze function to speed up the representation learning. Moreover, we introduce an auxiliary loss to ease the training of transformers using the supervision signals provided by all output tokens. Extensive experiments verify that our TrajFormer achieves a preferable speed-accuracy balance compared to existing approaches.

2. Sevi: Speech-to-Visualization through Neural Machine Translation

   Jiawei Tang , Yuyu Luo , Mourad Ouzzani , Guoliang Li , and Hongyang Chen

   In Proceedings of the 2022 International Conference on Management of Data , May 2022

   Abs

   Data visualization is a powerful tool for understating information through visual cues. However, allowing novices to create visualization artifacts for what they want to see is not easy, just as not everyone can write SQL queries. Arguably, the most natural way to specify what to visualize is through natural language or speech, similar to our daily search on Google or Apple Siri, leaving to the system the task of reasoning about what to visualize and how.In this demo, we present Sevi an end-to-end data visualization system that acts as a virtual assistant to allow novices to create visualizations through either natural language or speech. Sevi is powered by two main components: Speech2Text which is based on Google Cloud Speech-to-Text Rest API, and Text2VIS, which uses an end-to-end neural machine translation model called ncNet trained using a cross-domain benchmark called nvBench. Both ncNet and nvBench have been developed by us. We will walk the audience through two general domain datasets, one related to COVID-19 and the other on NBA player statistics, to highlight how Sevi enables novices to easily create data visualizations. Because nvBench contains Text2VIS training samples from 105 domains (e.g., sport, college, hospital, etc.), the audience can play with speech or text input with any of these domains.

3. Deep generative modeling and clustering of single cell Hi-C data

   Qiao Liu , Wanwen Zeng , Wei Zhang , Sicheng Wang , Hongyang Chen , Rui Jiang , Mu Zhou , and 1 more author

   Briefings in Bioinformatics, Dec 2022

   Abs

   Deciphering 3D genome conformation is important for understanding gene regulation and cellular function at a spatial level. The recent advances of single cell Hi-C technologies have enabled the profiling of the 3D architecture of DNA within individual cell, which allows us to study the cell-to-cell variability of 3D chromatin organization. Computational approaches are in urgent need to comprehensively analyze the sparse and heterogeneous single cell Hi-C data. Here, we proposed scDEC-Hi-C, a new framework for single cell Hi-C analysis with deep generative neural networks. scDEC-Hi-C outperforms existing methods in terms of single cell Hi-C data clustering and imputation. Moreover, the generative power of scDEC-Hi-C could help unveil the differences of chromatin architecture across cell types. We expect that scDEC-Hi-C could shed light on deepening our understanding of the complex mechanism underlying the formation of chromatin contacts.

4. Deep graph level anomaly detection with contrastive learning

   Xuexiong Luo , Jia Wu , Jian Yang , Shan Xue , Hao Peng , Chuan Zhou , Hongyang Chen , and 2 more authors

   Scientific Reports, Dec 2022