no code implementations • EMNLP (sdp) 2020 • Jiaxin Ju, Ming Liu, Longxiang Gao, Shirui Pan
The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.
1 code implementation • ACL 2022 • Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Reza Haf
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.
no code implementations • 27 May 2024 • Yixin Liu, Shiyuan Li, Yu Zheng, Qingfeng Chen, Chengqi Zhang, Shirui Pan
Graph anomaly detection (GAD), which aims to identify abnormal nodes that differ from the majority within a graph, has garnered significant attention.
1 code implementation • 27 May 2024 • Xin He, Wenqi Fan, Ruobing Wang, Yili Wang, Ying Wang, Shirui Pan, Xin Wang
More specifically, CGSoRec first includes a Condition-Guided Social Denoising Model (CSD) to remove redundant social relations in the social network for capturing users' social preferences with items more precisely.
no code implementations • 23 May 2024 • Hongzhi Zhang, Xiuwen Gong, Shirui Pan, Jia Wu, Bo Du, Wenbin Hu
In the drug development engineering field, predicting novel drug-target interactions is extremely crucial. However, although existing methods have achieved high accuracy levels in predicting known drugs and drug targets, they fail to utilize global protein information during DTI prediction.
no code implementations • 23 May 2024 • He Zhang, Bang Wu, Xiangwen Yang, Xingliang Yuan, Chengqi Zhang, Shirui Pan
Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data.
no code implementations • 23 May 2024 • Jiapu Wang, Kai Sun, Linhao Luo, Wei Wei, Yongli Hu, Alan Wee-Chung Liew, Shirui Pan, BaoCai Yin
To account for the evolving nature of TKGs, a dynamic adaptation strategy is proposed to update the LLM-generated rules with the latest events.
no code implementations • 23 May 2024 • Guibin Zhang, Xiangguo Sun, Yanwei Yue, Kun Wang, Tianlong Chen, Shirui Pan
Specifically, MoG incorporates multiple sparsifier experts, each characterized by unique sparsity levels and pruning criteria, and selects the appropriate experts for each node.
no code implementations • 23 May 2024 • Kun Li, Xiuwen Gong, Shirui Pan, Jia Wu, Bo Du, Wenbin Hu
As a result, we introduce regressor-free guidance molecule generation to ensure sampling within a more effective space and support DRP.
1 code implementation • 17 May 2024 • Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma, Dapeng Tao, Jia Wu, Wenbin Hu, Shirui Pan, Bo Du
To ensure masking uniformity of subgraphs across these scales, we propose a novel coarse-to-fine strategy that initiates masking at the coarsest scale and progressively back-projects the mask to the finer scales.
no code implementations • 13 May 2024 • Shilong Wang, Hao Wu, Yifan Duan, Guibin Zhang, Guohao Li, Yuxuan Liang, Shirui Pan, Kun Wang, Yang Wang
This assumption often poses challenges for many GNNs working with heterophilic graphs.
no code implementations • 6 May 2024 • Farid Saberi-Movahed, Kamal Berahman, Razieh Sheikhpour, Yuefeng Li, Shirui Pan
Dimensionality Reduction plays a pivotal role in improving feature learning accuracy and reducing training time by eliminating redundant features, noise, and irrelevant data.
1 code implementation • 29 Apr 2024 • Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen
Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks.
no code implementations • 24 Apr 2024 • Xu Shen, Yili Wang, Kaixiong Zhou, Shirui Pan, Xin Wang
In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs.
1 code implementation • 24 Apr 2024 • Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma, Shirui Pan, Wenbin Hu
Therefore, this paper presents Gradformer, a method innovatively integrating GT with the intrinsic inductive bias by applying an exponential decay mask to the attention matrix.
1 code implementation • 17 Apr 2024 • Zhaopeng Peng, Xiaoliang Fan, Yufan Chen, Zheng Wang, Shirui Pan, Chenglu Wen, Ruisheng Zhang, Cheng Wang
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs.
no code implementations • 28 Mar 2024 • Jiapu Wang, Zheng Cui, Boyue Wang, Shirui Pan, Junbin Gao, BaoCai Yin, Wen Gao
However, existing Temporal Knowledge Graph Completion (TKGC) methods either model TKGs in a single space or neglect the heterogeneity of different curvature spaces, thus constraining their capacity to capture these intricate geometric structures.
no code implementations • 21 Mar 2024 • Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen
Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.
1 code implementation • 15 Mar 2024 • Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, Shirui Pan
This enables the effective evaluation of the well-trained GNNs' ability to capture test node semantics and structural representations, making it an expressive metric for estimating the generalization error in online GNN evaluation.
no code implementations • 10 Mar 2024 • Xin Liu, Yuxiang Zhang, Meng Wu, Mingyu Yan, Kun He, Wei Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan
It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i. e., graph data augmentation and attack.
no code implementations • 28 Feb 2024 • Qin Zhang, Xiaowei Li, Jiexin Lu, Liping Qiu, Shirui Pan, Xiaojun Chen, Junyang Chen
In specific, ROG$_{PL}$ consists of two modules, i. e., denoising via label propagation and open-set prototype learning via regions.
no code implementations • 26 Feb 2024 • Man Wu, Xin Zheng, Qin Zhang, Xiao Shen, Xiong Luo, Xingquan Zhu, Shirui Pan
Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data.
no code implementations • 18 Feb 2024 • Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang
In long-term time series forecasting (LTSF) tasks, an increasing number of models have acknowledged that discrete time series originate from continuous dynamic systems and have attempted to model their dynamical structures.
no code implementations • 5 Feb 2024 • Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang, Yuxuan Liang, Bin Yang, Jindong Wang, Shirui Pan, Qingsong Wen
Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications.
no code implementations • 2 Feb 2024 • Guibin Zhang, Yanwei Yue, Kun Wang, Junfeng Fang, Yongduo Sui, Kai Wang, Yuxuan Liang, Dawei Cheng, Shirui Pan, Tianlong Chen
Specifically, GST initially constructs a topology & semantic anchor at a low training cost, followed by performing dynamic sparse training to align the sparse graph with the anchor.
no code implementations • 2 Feb 2024 • Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, Gholamreza Haffari
Large language models (LLMs) are not amenable to frequent re-training, due to high training costs arising from their massive scale.
no code implementations • 11 Jan 2024 • Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa T. Phan, Yi-Ping Phoebe Chen, Shirui Pan, Wei Xiang
However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values.
1 code implementation • 10 Jan 2024 • Luzhi Wang, Dongxiao He, He Zhang, Yixin Liu, Wenjie Wang, Shirui Pan, Di Jin, Tat-Seng Chua
To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN.
1 code implementation • 18 Dec 2023 • Zhangchi Qiu, Ye Tao, Shirui Pan, Alan Wee-Chung Liew
In our KERL framework, entity textual descriptions are encoded via a pre-trained language model, while a knowledge graph helps reinforce the representation of these entities.
1 code implementation • 14 Dec 2023 • Bo Xiong, Mojtaba Nayyeri, Linhao Luo, ZiHao Wang, Shirui Pan, Steffen Staab
NestE represents each atomic fact as a $1\times3$ matrix, and each nested relation is modeled as a $3\times3$ matrix that rotates the $1\times3$ atomic fact matrix through matrix multiplication.
1 code implementation • 13 Dec 2023 • Bang Wu, He Zhang, Xiangwen Yang, Shuo Wang, Minhui Xue, Shirui Pan, Xingliang Yuan
These limitations call for an effective and comprehensive solution that detects and mitigates data misuse without requiring exact training data while respecting the proprietary nature of such data.
no code implementations • 11 Dec 2023 • Jiaxu Zhao, Meng Fang, Shirui Pan, Wenpeng Yin, Mykola Pechenizkiy
In this work, we propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs (e. g., GPT-4 \cite{openai2023gpt4}) to assess bias in models.
no code implementations • NeurIPS 2023 • Yixin Liu, Kaize Ding, Qinghua Lu, Fuyi Li, Leo Yu Zhang, Shirui Pan
In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i. e., the vital subgraph that leads to the predictions.
1 code implementation • 18 Oct 2023 • Junjun Pan, Yixin Liu, Yizhen Zheng, Shirui Pan
Comprising two modules - a pre-processing module and an ego-neighbor matching module - PREM eliminates the necessity for message-passing propagation during training, and employs a simple contrastive loss, leading to considerable reductions in training time and memory usage.
1 code implementation • 17 Oct 2023 • Yuxi Wei, Juntong Peng, Tong He, Chenxin Xu, Jian Zhang, Shirui Pan, Siheng Chen
To analyze multivariate time series, most previous methods assume regular subsampling of time series, where the interval between adjacent measurements and the number of samples remain unchanged.
5 code implementations • 16 Oct 2023 • Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, XiaoLi Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
1 code implementation • 12 Oct 2023 • Yizhen Zheng, Huan Yee Koh, Jiaxin Ju, Anh T. N. Nguyen, Lauren T. May, Geoffrey I. Webb, Shirui Pan
We present a method for using general-purpose large language models to make inferences from scientific datasets of the form usually associated with special-purpose machine learning algorithms.
no code implementations • 9 Oct 2023 • Shirui Pan, Yizhen Zheng, Yixin Liu
Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and more.
2 code implementations • 3 Oct 2023 • Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
1 code implementation • 2 Oct 2023 • Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan
In this paper, we propose a novel method called reasoning on graphs (RoG) that synergizes LLMs with KGs to enable faithful and interpretable reasoning.
1 code implementation • 20 Sep 2023 • Xin Zheng, Yixin Liu, Zhifeng Bao, Meng Fang, Xia Hu, Alan Wee-Chung Liew, Shirui Pan
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years.
no code implementations • 16 Sep 2023 • Dongran Yu, Xueyan Liu, Shirui Pan, Anchen Li, Bo Yang
A key objective in field of artificial intelligence is to develop cognitive models that can exhibit human-like intellectual capabilities.
no code implementations • 14 Sep 2023 • Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang, Chao Chen, Shirui Pan, Kok-Leong Ong, Jun Zhang, Yang Xiang
For the first time, we show the feasibility of a client-side adversary with limited knowledge being able to recover the training samples from the aggregated global model.
1 code implementation • 4 Sep 2023 • Linhao Luo, Jiaxin Ju, Bo Xiong, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).
1 code implementation • 31 Aug 2023 • Xiao Shen, Shirui Pan, Kup-Sze Choi, Xi Zhou
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently.
no code implementations • 18 Aug 2023 • Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Our research shows the potential of contrastive representation learning to advance time series anomaly detection.
no code implementations • 10 Aug 2023 • Qin Zhang, Zelin Shi, Xiaolin Zhang, Xiaojun Chen, Philippe Fournier-Viger, Shirui Pan
Node classification is the task of predicting the labels of unlabeled nodes in a graph.
1 code implementation • 4 Aug 2023 • Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, BaoCai Yin, Wen Gao
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
1 code implementation • 17 Jul 2023 • Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xiang
To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time series anomaly detection.
1 code implementation • 7 Jul 2023 • Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
1 code implementation • 26 Jun 2023 • Zicheng Zhao, Linhao Luo, Shirui Pan, Quoc Viet Hung Nguyen, Chen Gong
Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities.
1 code implementation • 16 Jun 2023 • Kexin Zhang, Qingsong Wen, Chaoli Zhang, Rongyao Cai, Ming Jin, Yong liu, James Zhang, Yuxuan Liang, Guansong Pang, Dongjin Song, Shirui Pan
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
no code implementations • 14 Jun 2023 • Shirui Pan, Linhao Luo, YuFei Wang, Chen Chen, Jiapu Wang, Xindong Wu
In this article, we present a forward-looking roadmap for the unification of LLMs and KGs.
1 code implementation • 13 Jun 2023 • Yizhen Zheng, He Zhang, Vincent CS Lee, Yu Zheng, Xiao Wang, Shirui Pan
Real-world graphs generally have only one kind of tendency in their connections.
1 code implementation • NeurIPS 2023 • Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, Shirui Pan
Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.
1 code implementation • 3 Jun 2023 • Bo Xiong, Mojtaba Nayyer, Shirui Pan, Steffen Staab
Although some recent works have proposed to embed hyper-relational KGs, these methods fail to capture essential inference patterns of hyper-relational facts such as qualifier monotonicity, qualifier implication, and qualifier mutual exclusion, limiting their generalization capability.
no code implementations • 1 Jun 2023 • Di Jin, Luzhi Wang, He Zhang, Yizhen Zheng, Weiping Ding, Feng Xia, Shirui Pan
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era.
1 code implementation • 29 May 2023 • Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan
Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
no code implementations • 21 May 2023 • Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan, Philip S. Yu
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
no code implementations • 11 May 2023 • Ming Jin, Guangsi Shi, Yuan-Fang Li, Qingsong Wen, Bo Xiong, Tian Zhou, Shirui Pan
In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs.
1 code implementation • 10 May 2023 • Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan
We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.
1 code implementation • 6 May 2023 • Dongwon Kelvin Ryu, Meng Fang, Shirui Pan, Gholamreza Haffari, Ehsan Shareghi
Text-based games (TGs) are language-based interactive environments for reinforcement learning.
no code implementations • 4 May 2023 • Fatemeh Shiri, Teresa Wang, Shirui Pan, Xiaojun Chang, Yuan-Fang Li, Reza Haffari, Van Nguyen, Shuang Yu
In order to exploit the potentially useful and rich information from such sources, it is necessary to extract not only the relevant entities and concepts but also their semantic relations, together with the uncertainty associated with the extracted knowledge (i. e., in the form of probabilistic knowledge graphs).
no code implementations • 24 Apr 2023 • Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.
Hierarchical Multi-label Classification Knowledge Graph Completion +1
1 code implementation • 17 Apr 2023 • Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan
In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC).
no code implementations • 3 Mar 2023 • Junbin Mao, Jin Liu, Hanhe Lin, Hulin Kuang, Shirui Pan, Yi Pan
To effectively offset the negative impact between modalities in the process of multi-modal integration and extract heterogeneous information from graphs, we propose a novel method called MMKGL (Multi-modal Multi-Kernel Graph Learning).
no code implementations • 23 Feb 2023 • Xin Zheng, Miao Zhang, Chunyang Chen, Qin Zhang, Chuan Zhou, Shirui Pan
Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.
no code implementations • 30 Jan 2023 • He Zhang, Xingliang Yuan, Shirui Pan
In this paper, we pioneer the exploration of the interaction between the privacy risks of edge leakage and the individual fairness of a GNN.
no code implementations • 22 Dec 2022 • Yuanzhe Zhang, Shirui Pan, Jiangshan Yu
Blockchain sharding is a promising approach to this problem.
1 code implementation • 25 Nov 2022 • Yixin Liu, Yizhen Zheng, Daokun Zhang, Vincent CS Lee, Shirui Pan
Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges.
1 code implementation • 15 Nov 2022 • Linhao Luo, Reza Haffari, Shirui Pan
Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation that models the link prediction on dynamic graphs as a dynamic-changing stochastic process.
no code implementations • 10 Nov 2022 • Haiyang Lin, Mingyu Yan, Xiaochun Ye, Dongrui Fan, Shirui Pan, WenGuang Chen, Yuan Xie
This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training.
1 code implementation • 9 Nov 2022 • Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare.
1 code implementation • 8 Nov 2022 • Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan
As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.
1 code implementation • 30 Oct 2022 • Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, Shirui Pan
For long document abstractive models, we show that the constant strive for state-of-the-art ROUGE results can lead us to generate more relevant summaries but not factual ones.
1 code implementation • 17 Oct 2022 • Yizhen Zheng, Yu Zheng, Xiaofei Zhou, Chen Gong, Vincent CS Lee, Shirui Pan
To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short).
no code implementations • 2 Sep 2022 • Xin Liu, Xunbin Xiong, Mingyu Yan, Runzhen Xue, Shirui Pan, Xiaochun Ye, Dongrui Fan
Thereby, we propose to drop redundancy and improve efficiency of training large-scale graphs with GNNs, by rethinking the inherent characteristics in a graph.
no code implementations • 10 Aug 2022 • Guangyuan Shen, Dehong Gao, Duanxiao Song, Libin Yang, Xukai Zhou, Shirui Pan, Wei Lou, Fang Zhou
We present a novel clustering-based client selection scheme to accelerate the FL convergence by variance reduction.
2 code implementations • 6 Jul 2022 • Xiaocheng Yang, Mingyu Yan, Shirui Pan, Xiaochun Ye, Dongrui Fan
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations.
Ranked #8 on Node Property Prediction on ogbn-mag
1 code implementation • 3 Jul 2022 • Huan Yee Koh, Jiaxin Ju, Ming Liu, Shirui Pan
The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics.
1 code implementation • 25 Jun 2022 • Shichao Zhu, Chuan Zhou, Anfeng Cheng, Shirui Pan, Shuaiqiang Wang, Dawei Yin, Bin Wang
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.
no code implementations • CVPR 2022 • Mingjie Li, Wenjia Cai, Karin Verspoor, Shirui Pan, Xiaodan Liang, Xiaojun Chang
To endow models with the capability of incorporating expert knowledge, we propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG), in which clinical relation triples are injected into the visual features as prior knowledge to drive the decoding procedure.
1 code implementation • 3 Jun 2022 • Yizhen Zheng, Shirui Pan, Vincent CS Lee, Yu Zheng, Philip S. Yu
Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss.
no code implementations • 1 Jun 2022 • Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, Steffen Staab
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
1 code implementation • 30 May 2022 • Di Jin, Luzhi Wang, Yizhen Zheng, Xiang Li, Fei Jiang, Wei Lin, Shirui Pan
As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score.
no code implementations • 16 May 2022 • He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics.
no code implementations • 28 Apr 2022 • Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang, Tung Kieu, Xuanyi Dong, Shirui Pan
(i) Linear complexity: we introduce a novel patch attention with linear complexity.
1 code implementation • 29 Mar 2022 • Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, Shirui Pan
Such spatio-temporal agnostic models employ a shared parameter space irrespective of the time series locations and the time periods and they assume that the temporal patterns are similar across locations and do not evolve across time, which may not always hold, thus leading to sub-optimal results.
no code implementations • 21 Mar 2022 • Fatemeh Shiri, Terry Yue Zhuo, Zhuang Li, Van Nguyen, Shirui Pan, Weiqing Wang, Reza Haffari, Yuan-Fang Li
In this paper, we investigate how to exploit paraphrasing methods for the automated generation of large-scale training datasets (in the form of paraphrased utterances and their corresponding logical forms in SQL format) and present our experimental results using real-world data in the maritime domain.
1 code implementation • 25 Feb 2022 • Linhao Luo, Yumeng Li, Buyu Gao, Shuai Tang, Sinan Wang, Jiancheng Li, Tanchao Zhu, Jiancai Liu, Zhao Li, Shirui Pan
We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation.
no code implementations • 25 Feb 2022 • He Zhang, Xingliang Yuan, Chuan Zhou, Shirui Pan
By projecting the strategy, our method dramatically minimizes the cost of learning a new attack strategy when the attack budget changes.
1 code implementation • 17 Feb 2022 • Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
no code implementations • 14 Feb 2022 • Xin Zheng, Yi Wang, Yixin Liu, Ming Li, Miao Zhang, Di Jin, Philip S. Yu, Shirui Pan
In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.
no code implementations • 11 Feb 2022 • Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
1 code implementation • 10 Feb 2022 • Bingxin Zhou, Yuanhong Jiang, Yu Guang Wang, Jingwei Liang, Junbin Gao, Shirui Pan, Xiaoqun Zhang
The performance of graph representation learning is affected by the quality of graph input.
no code implementations • 10 Feb 2022 • Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie
Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.
1 code implementation • 24 Jan 2022 • Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng Wang
Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks.
no code implementations • 19 Jan 2022 • Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S. Yu, Guandong Xu
In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
1 code implementation • 17 Jan 2022 • Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan
To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.
no code implementations • 15 Jan 2022 • Guangyuan Shen, Dehong Gao, Libin Yang, Fang Zhou, Duanxiao Song, Wei Lou, Shirui Pan
However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL.
no code implementations • CVPR 2022 • Dongran Yu, Bo Yang, Qianhao Wei, Anchen Li, Shirui Pan
In particular, BPGR can also provide easy-to-understand insights for reasoning results to show interpretability.
no code implementations • NeurIPS 2021 • Sheng Wan, Yibing Zhan, Liu Liu, Baosheng Yu, Shirui Pan, Chen Gong
Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph.
no code implementations • CVPR 2022 • Miao Zhang, Jilin Hu, Steven Su, Shirui Pan, Xiaojun Chang, Bin Yang, Gholamreza Haffari
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation.
no code implementations • 25 Nov 2021 • Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu
However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.
no code implementations • 20 Nov 2021 • Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li
To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.
no code implementations • 10 Nov 2021 • Dongran Yu, Bo Yang, Dayou Liu, Hui Wang, Shirui Pan
In recent years, neural systems have demonstrated highly effective learning ability and superior perception intelligence.
1 code implementation • 17 Oct 2021 • Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan
We present and implement two types of attacks, i. e., training-based attacks and threshold-based attacks from different adversarial capabilities.
no code implementations • Findings (EMNLP) 2021 • Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, Shirui Pan
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.
no code implementations • 29 Sep 2021 • Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.
no code implementations • 27 Sep 2021 • Xu Yan, Xiaoliang Fan, Peizhen Yang, Zonghan Wu, Shirui Pan, Longbiao Chen, Yu Zang, Cheng Wang
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems.
no code implementations • 20 Sep 2021 • Xin Zheng, Yanbo Fan, Baoyuan Wu, Yong Zhang, Jue Wang, Shirui Pan
Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications.
1 code implementation • 25 Aug 2021 • Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.
1 code implementation • 10 Jul 2021 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang
Second, the bandwidth of existing graph convolutional filters is fixed.
1 code implementation • 21 Jun 2021 • Miao Zhang, Steven Su, Shirui Pan, Xiaojun Chang, Ehsan Abbasnejad, Reza Haffari
A key challenge to the scalability and quality of the learned architectures is the need for differentiating through the inner-loop optimisation.
Ranked #22 on Neural Architecture Search on NAS-Bench-201, CIFAR-10
1 code implementation • 18 Jun 2021 • Yixin Liu, Shirui Pan, Yu Guang Wang, Fei Xiong, Liang Wang, Qingfeng Chen, Vincent CS Lee
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.
1 code implementation • 6 Jun 2021 • Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, Steffen Staab
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
1 code implementation • 12 May 2021 • Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.
no code implementations • 3 May 2021 • Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, Huan Liu
In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.
no code implementations • 8 Mar 2021 • Man Wu, Shirui Pan, Lan Du, Xingquan Zhu
By generating multiple graphs at different distance levels, based on the adjacency matrix, we develop a long-short distance attention model to model these graphs.
3 code implementations • 27 Feb 2021 • Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, Philip S. Yu
Deep learning on graphs has attracted significant interests recently.
1 code implementation • 27 Feb 2021 • Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, George Karypis
Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.
1 code implementation • 26 Feb 2021 • Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, Bin Wang
In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP).
no code implementations • 25 Feb 2021 • Shaoxiong Ji, Yue Tan, Teemu Saravirta, Zhiqin Yang, Yixin Liu, Lauri Vasankari, Shirui Pan, Guodong Long, Anwar Walid
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
no code implementations • 3 Jan 2021 • Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
1 code implementation • NeurIPS 2020 • Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.
1 code implementation • NeurIPS 2020 • Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, ZongYuan Ge, Steven Su
A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search.
no code implementations • 24 Nov 2020 • Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, Jiajun Bu
To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.
1 code implementation • 24 Oct 2020 • Bang Wu, Xiangwen Yang, Shirui Pan, Xingliang Yuan
Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client.
1 code implementation • NeurIPS 2020 • Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang
To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.
no code implementations • 19 Oct 2020 • Jiaxin Ju, Ming Liu, Longxiang Gao, Shirui Pan
The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.
no code implementations • Findings (ACL) 2021 • Shaoxiong Ji, Shirui Pan, Pekka Marttinen
However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes.
no code implementations • 19 Sep 2020 • Sheng Wan, Chen Gong, Shirui Pan, Jie Yang, Jian Yang
Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification.
no code implementations • 15 Sep 2020 • Sheng Wan, Shirui Pan, Jian Yang, Chen Gong
Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph.
no code implementations • 9 Aug 2020 • Jin Xu, Shuo Yu, Ke Sun, Jing Ren, Ivan Lee, Shirui Pan, Feng Xia
Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.
1 code implementation • 1 Jul 2020 • Yue Yuan, Xiaofei Zhou, Shirui Pan, Qiannan Zhu, Zeliang Song, Li Guo
Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts.
Ranked #11 on Relation Extraction on WebNLG
2 code implementations • 24 May 2020 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #1 on Univariate Time Series Forecasting on Electricity
1 code implementation • 2 Feb 2020 • Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
no code implementations • 28 Nov 2019 • Mingjiang Liang, Shaoli Huang, Shirui Pan, Mingming Gong, Wei Liu
Few-shot learning is currently enjoying a considerable resurgence of interest, aided by the recent advance of deep learning.
no code implementations • 23 Oct 2019 • Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, Zi Huang
Suicide is a critical issue in modern society.
no code implementations • 26 Sep 2019 • Sheng Wan, Chen Gong, Ping Zhong, Shirui Pan, Guangyu Li, Jian Yang
In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance.
no code implementations • 22 Jul 2019 • Miao Zhang, Huiqi Li, Shirui Pan, Taoping Liu, Steven Su
The best architecture obtained by our algorithm with the same search space achieves the state-of-the-art test error rate of 2. 51\% on CIFAR-10 with only 7. 5 hours search time in a single GPU, and a validation perplexity of 60. 02 and a test perplexity of 57. 36 on PTB.
3 code implementations • 15 Jun 2019 • Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang
Graph clustering is a fundamental task which discovers communities or groups in networks.
Ranked #8 on Node Clustering on Cora
8 code implementations • 31 May 2019 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
Ranked #5 on Traffic Prediction on NE-BJ
1 code implementation • 4 Apr 2019 • Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long
In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems.
Ranked #25 on Graph Classification on NCI1
no code implementations • 4 Jan 2019 • Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang
Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively.
Ranked #7 on Node Clustering on Cora
5 code implementations • 3 Jan 2019 • Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.
4 code implementations • 17 Dec 2018 • Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.
2 code implementations • ICDM 2018 • Hong Yang, Shirui Pan, Peng Zhang, Ling Chen, Defu Lian, Chengqi Zhang
To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.
Ranked #1 on Link Prediction on Wiki
no code implementations • 3 May 2018 • Yaxin Shi, Donna Xu, Yuangang Pan, Ivor W. Tsang, Shirui Pan
In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges.
4 code implementations • 13 Feb 2018 • Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Ranked #5 on Link Prediction on Pubmed
no code implementations • CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management 2017 • Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang
In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
3 code implementations • 14 Sep 2017 • Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, Chengqi Zhang
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively.
Ranked #68 on Natural Language Inference on SNLI
no code implementations • 19 Aug 2016 • Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.