no code implementations • 7 Jun 2024 • Shuo Yu, Fayez Alqahtani, Amr Tolba, Ivan Lee, Tao Jia, Feng Xia
The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly.
no code implementations • 7 Jun 2024 • Shuo Yu, Feng Xia, Yueru Wang, Shihao Li, Falih Febrinanto, Madhu Chetty
To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network.
no code implementations • 7 Jun 2024 • Zhen Cai, Tao Tang, Shuo Yu, Yunpeng Xiao, Feng Xia
We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates.
no code implementations • 7 Jun 2024 • Xu Yuan, Na Zhou, Shuo Yu, Huafei Huang, Zhikui Chen, Feng Xia
Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks.
no code implementations • 27 May 2024 • Renqiang Luo, Huafei Huang, Shuo Yu, Zhuoyang Han, Estrid He, Xiuzhen Zhang, Feng Xia
We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectrum.
no code implementations • 3 May 2024 • Zhanzhong Gu, Xiangjian He, Gengfa Fang, Chengpei Xu, Feng Xia, Wenjing Jia
Finally, we deploy our system on a movable robot-mounted edge computing platform, achieving flexible healthcare monitoring in real-world scenarios.
1 code implementation • 26 Apr 2024 • Renqiang Luo, Huafei Huang, Shuo Yu, Xiuzhen Zhang, Feng Xia
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups.
no code implementations • 26 Apr 2024 • Renqiang Luo, Tao Tang, Feng Xia, Jiaying Liu, Chengpei Xu, Leo Yu Zhang, Wei Xiang, Chengqi Zhang
Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups.
no code implementations • 13 Apr 2024 • Chengpei Xu, Hao Fu, Long Ma, Wenjing Jia, Chengqi Zhang, Feng Xia, Xiaoyu Ai, Binghao Li, Wenjie Zhang
Localizing text in low-light environments is challenging due to visual degradations.
no code implementations • 9 Feb 2024 • Yemeng Liu, Jing Ren, Jianshuo Xu, Xiaomei Bai, Roopdeep Kaur, Feng Xia
However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze angle, body posture, and background information in the task of cheating behavior detection.
no code implementations • 7 Feb 2024 • Ciyuan Peng, Jiayuan He, Feng Xia
This survey paper conducts a comparative analysis of existing works in multimodal graph learning, elucidating how multimodal learning is achieved across different graph types and exploring the characteristics of prevalent learning techniques.
no code implementations • 6 Feb 2024 • Huiling Tu, Shuo Yu, Vidya Saikrishna, Feng Xia, Karin Verspoor
Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains.
1 code implementation • 6 Feb 2024 • Xin Chen, Mingliang Hou, Tao Tang, Achhardeep Kaur, Feng Xia
With the arrival of the big data era, mobility profiling has become a viable method of utilizing enormous amounts of mobility data to create an intelligent transportation system.
1 code implementation • 30 Dec 2023 • Falih Gozi Febrinanto, Mujie Liu, Feng Xia
In this work, we proposed Bargrain (balanced graph structure for brains), which models two graph structures: filtered correlation matrix and optimal sample graph using graph convolution networks (GCNs).
no code implementations • 15 Dec 2023 • Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa, Mujie Liu, Vidya Saikrishna, Jiangang Ma, Feng Xia
Many multivariate time series anomaly detection frameworks have been proposed and widely applied.
no code implementations • 28 Sep 2023 • Chong Liu, Xiaoyang Liu, Lixin Zhang, Feng Xia, Leyu Lin
Due to the lack of supervised signals in click confidence, we first apply self-supervised learning to obtain click confidence scores via a global self-distillation method.
no code implementations • 15 Aug 2023 • Chong Liu, Xiaoyang Liu, Ruobing Xie, Lixin Zhang, Feng Xia, Leyu Lin
A powerful positive item augmentation is beneficial to address the sparsity issue, while few works could jointly consider both the accuracy and diversity of these augmented training labels.
no code implementations • 12 Jun 2023 • Feng Xia, Xin Chen, Shuo Yu, Mingliang Hou, Mujie Liu, Linlin You
To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships.
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.
no code implementations • 29 May 2023 • Shuai Yang, Lixin Zhang, Feng Xia, Leyu Lin
Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to the convergence of candidates for users and the lack of system-level diversity.
no code implementations • 10 May 2023 • Chakkrit Termritthikun, Ayaz Umer, Suwichaya Suwanwimolkul, Feng Xia, Ivan Lee
To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2
no code implementations • 24 Mar 2023 • Ciyuan Peng, Feng Xia, Mehdi Naseriparsa, Francesco Osborne
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately.
1 code implementation • 5 Feb 2023 • JunJie Huang, Qi Cao, Ruobing Xie, Shaoliang Zhang, Feng Xia, HuaWei Shen, Xueqi Cheng
To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance.
no code implementations • 2 Feb 2023 • Shuo Yu, Ciyuan Peng, Yingbo Wang, Ahsan Shehzad, Feng Xia, Edwin R. Hancock
However, facilitating quantum theory to enhance graph learning is in its infancy.
no code implementations • 11 Dec 2022 • Jing Ren, Feng Xia, Azadeh Noori Hoshyar, Charu C. Aggarwal
Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades.
no code implementations • 27 Oct 2022 • Zhongyi Zhang, Guixia Li, Ziqiang Wang, Feng Xia, Ning Zhao, Huibin Nie, Zezhong Ye, Joshua Lin, Yiyi Hui, Xiangchun Liu
To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements.
1 code implementation • 12 Oct 2022 • Ting Jiang, Deqing Wang, Fuzhen Zhuang, Ruobing Xie, Feng Xia
These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights.
no code implementations • 19 Sep 2022 • Ruobing Xie, Lin Ma, Shaoliang Zhang, Feng Xia, Leyu Lin
Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time.
no code implementations • 6 Apr 2022 • Feng Xia, Shuo Yu, Chengfei Liu, Ivan Lee
In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs.
1 code implementation • 17 Mar 2022 • Shuo Yu, Huafei Huang, Minh N. Dao, Feng Xia
To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks.
no code implementations • 23 Feb 2022 • Jiaying Liu, Jing Ren, Wenqing Zheng, Lianhua Chi, Ivan Lee, Feng Xia
In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science.
no code implementations • 23 Feb 2022 • Jiaying Liu, Feng Xia, Xu Feng, Jing Ren, Huan Liu
To address this open issue, we propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks.
no code implementations • 22 Feb 2022 • Ciyuan Peng, Feng Xia, Vidya Saikrishna, Huan Liu
The graph learning models suffer from the inability to efficiently learn graph information.
no code implementations • 22 Feb 2022 • Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, Charu Aggarwal
Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure.
no code implementations • 18 Feb 2022 • Teng Guo, Linhong Li, Dongyu Zhang, Feng Xia
In this paper, we develop a general data-driven method for identifying students' sleep patterns according to their Internet access pattern stored in the education management system and explore its influence from various aspects.
no code implementations • 17 Feb 2022 • Xiangjie Kong, Kailai Wang, Mingliang Hou, Feng Xia, Gour Karmakar, JianXin Li
To reduce this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN).
no code implementations • 17 Feb 2022 • Xiangjie Kong, Jun Zhang, Da Zhang, Yi Bu, Ying Ding, Feng Xia
Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success.
no code implementations • 16 Feb 2022 • Honglong Chen, Zhe Li, Zhu Wang, Zhichen Ni, Junjian Li, Ge Xu, Abdul Aziz, Feng Xia
As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data.
no code implementations • 16 Feb 2022 • Zhu Wang, Honglong Chen, Zhe Li, Kai Lin, Nan Jiang, Feng Xia
Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items.
no code implementations • 16 Feb 2022 • Zhichen Ni, Honglong Chen, Zhe Li, Xiaomeng Wang, Na Yan, Weifeng Liu, Feng Xia
The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge.
no code implementations • 16 Feb 2022 • Feng Xia, Lei Wang, Tao Tang, Xin Chen, Xiangjie Kong, Giles Oatley, Irwin King
In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices.
no code implementations • 16 Feb 2022 • Xiangtai Chen, Tao Tang, Jing Ren, Ivan Lee, Honglong Chen, Feng Xia
We devise an unsupervised learning model called HAI (Heterogeneous graph Attention InfoMax) which aggregates attention mechanism and mutual information for institution recommendation.
1 code implementation • NeurIPS 2021 • Hong Chen, Yudong Chen, Xin Wang, Ruobing Xie, Rui Wang, Feng Xia, Wenwu Zhu
However, learning such disentangled representations from multi-feedback data is challenging because i) multi-feedback is complex: there exist complex relations among different types of feedback (e. g., click, unclick, and dislike, etc) as well as various user intentions, and ii) multi-feedback is noisy: there exists noisy (useless) information both in features and labels, which may deteriorate the recommendation performance.
no code implementations • 11 Oct 2021 • Jing Ren, Feng Xia, Yemeng Liu, Ivan Lee
Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection.
1 code implementation • 20 Jul 2021 • Shuting Jin, Xiangxiang Zeng, Wei Huang, Feng Xia, Changzhi Jiang, Xiangrong Liu, Shaoliang Peng
The Corona Virus Disease 2019 (COVID-19) belongs to human coronaviruses (HCoVs), which spreads rapidly around the world.
1 code implementation • 15 Jul 2021 • Qiongqiong Liu, Tianqiao Liu, Jiafu Zhao, Qiang Fang, Wenbiao Ding, Zhongqin Wu, Feng Xia, Jiliang Tang, Zitao Liu
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
no code implementations • IEEE 2021 • Fangming Zhong∗, Guangze Wang, Zhikui Chen, Xu Yuan, Feng Xia
Generalized zero-shot learning (GZSL) has attracted consid- erable attention recently, which trains models with data from seen classes and tests on data from both seen and unseen classes.
no code implementations • 8 May 2021 • Ruobing Xie, Yalong Wang, Rui Wang, Yuanfu Lu, Yuanhang Zou, Feng Xia, Leyu Lin
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks).
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.
1 code implementation • WSDM 2021 • Ruobing Xie, Rui Wang, Shaoliang Zhang, Zhihong Yang, Feng Xia, Leyu Lin Authors Info & Claims
When finishing reading an item, users may want to access more relevant items related to the last read one as extended reading.
no code implementations • 7 Mar 2021 • Ke Sun, Lei Wang, Bo Xu, Wenhong Zhao, Shyh Wei Teng, Feng Xia
Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data.
no code implementations • 7 Mar 2021 • Ke Sun, Jiaying Liu, Shuo Yu, Bo Xu, Feng Xia
Features representation leverages the great power in network analysis tasks.
no code implementations • 5 Mar 2021 • Jing Ren, Feng Xia, Xiangtai Chen, Jiaying Liu, Mingliang Hou, Ahsan Shehzad, Nargiz Sultanova, Xiangjie Kong
Based on the preference list access, matching problems are divided into two categories, i. e., explicit matching and implicit matching.
Information Retrieval Recommendation Systems Social and Information Networks
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.
no code implementations • 27 Aug 2020 • Shuo Yu, Feng Xia, Jin Xu, Zhikui Chen, Ivan Lee
In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined.
no code implementations • 20 Aug 2020 • Jiaying Liu, Tao Tang, Xiangjie Kong, Amr Tolba, Zafer AL-Makhadmeh, Feng Xia
Advisor-advisee relationship is important in academic networks due to its universality and necessity.
no code implementations • 17 Aug 2020 • Jiaying Liu, Feng Xia, Lei Wang, Bo Xu, Xiangjie Kong, Hanghang Tong, Irwin King
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines.
no code implementations • 10 Aug 2020 • Xiaomei Bai, Ivan Lee, Zhaolong Ning, Amr Tolba, Feng Xia
Quantifying the impact of scientific papers objectively is crucial for research output assessment, which subsequently affects institution and country rankings, research funding allocations, academic recruitment and national/international scientific priorities.
no code implementations • 10 Aug 2020 • Xiaomei Bai, Mengyang Wang, Ivan Lee, Zhuo Yang, Xiangjie Kong, Feng Xia
The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation.
no code implementations • 9 Aug 2020 • Ke Hou, Jiaying Liu, Yin Peng, Bo Xu, Ivan Lee, Feng Xia
Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks.
no code implementations • 9 Aug 2020 • Hayat Dino Bedru, Shuo Yu, Xinru Xiao, Da Zhang, Liangtian Wan, He guo, Feng Xia
This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network.
no code implementations • 9 Aug 2020 • Feng Xia, Jiaying Liu, Hansong Nie, Yonghao Fu, Liangtian Wan, Xiangjie Kong
In this paper, we aim to provide a comprehensive review of classical random walks and quantum walks.
no code implementations • 9 Aug 2020 • Lei Wang, Jing Ren, Bo Xu, Jian-Xin Li, Wei Luo, Feng Xia
Link prediction plays an important role in network analysis and applications.
no code implementations • 9 Aug 2020 • Feng Xia, Nana Yaw Asabere, Haifeng Liu, Zhen Chen, Wei Wang
As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants.
no code implementations • 9 Aug 2020 • Nana Yaw Asabere, Feng Xia, Wei Wang, Joel J. P. C. Rodrigues, Filippo Basso, Jianhua Ma
This research addresses recommending presentation sessions at smart conferences to participants.
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.
no code implementations • 27 Dec 2019 • Teng Guo, Feng Xia, Shihao Zhen, Xiaomei Bai, Dongyu Zhang, Zitao Liu, Jiliang Tang
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
no code implementations • IJCNLP 2019 • Dongyu Zhang, Heting Zhang, Xikai Liu, Hongfei Lin, Feng Xia
To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor.
no code implementations • 24 Dec 2013 • Fengqi Li, Chuang Yu, Nanhai Yang, Feng Xia, Guangming Li, Fatemeh Kaveh-Yazdy
Transductive graph-based semi-supervised learning methods usually build an undirected graph utilizing both labeled and unlabeled samples as vertices.