1 code implementation • 26 Apr 2022 • Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, George H. Chen, Zhihao Jia, Philip S. Yu
PyGOD is an open-source Python library for detecting outliers on graph data.
no code implementations • 29 Mar 2022 • Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
Recently, increasing attention has been devoted to the graph few-shot learning problem, where the target novel classes only contain a few labeled nodes.
no code implementations • 17 Mar 2022 • Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu
In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.
no code implementations • 17 Feb 2022 • Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu
Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizable, transferable, and robust node representations in a self-supervised fashion.
no code implementations • 16 Feb 2022 • Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu
To counter the data noise and data scarcity issues in deep graph learning (DGL), increasing graph data augmentation research has been conducted lately.
no code implementations • 28 Dec 2021 • Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.
1 code implementation • 23 Dec 2021 • Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems.
1 code implementation • 18 Dec 2021 • Kaize Ding, Jianling Wang, James Caverlee, Huan Liu
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks.
no code implementations • EMNLP 2021 • Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
1 code implementation • 13 Jul 2021 • Jianling Wang, Kaize Ding, James Caverlee
A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items.
no code implementations • 12 Jun 2021 • Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.
no code implementations • 31 May 2021 • Ujun Jeong, Kaize Ding, Huan Liu
The growing use of social media has led to drastic changes in our decision-making.
1 code implementation • 26 Apr 2021 • Yushun Dong, Kaize Ding, Brian Jalaian, Shuiwang Ji, Jundong Li
Existing efforts can be mainly categorized as spectral-based and spatial-based methods.
no code implementations • 22 Feb 2021 • Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu
Network anomaly detection aims to find network elements (e. g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority.
1 code implementation • 8 Dec 2020 • Kai Shu, Yichuan Li, Kaize Ding, Huan Liu
The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy.
1 code implementation • EMNLP 2020 • Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, Huan Liu
Text classification is a critical research topic with broad applications in natural language processing.
no code implementations • 14 Jul 2020 • Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora Nazer, Kaize Ding, Mansooreh Karami, Huan Liu
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
1 code implementation • 10 Jul 2020 • Xuan Shan, Chuanjie Liu, Yiqian Xia, Qi Chen, Yusi Zhang, Kaize Ding, Yaobo Liang, Angen Luo, Yuxiang Luo
Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval.
1 code implementation • 23 Jun 2020 • Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu
By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.
no code implementations • 19 Aug 2019 • Kaize Ding, Yichuan Li, Jundong Li, Chenghao Liu, Huan Liu
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks.
1 code implementation • 2019 SIAM International Conference on Data Mining (SDM) 2019 • Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu
In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data.