no code implementations • Findings (EMNLP) 2021 • Zixuan Zhang, Hongwei Wang, Han Zhao, Hanghang Tong, Heng Ji
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.
no code implementations • EMNLP 2020 • Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu
Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.
1 code implementation • NAACL (TextGraphs) 2021 • Qi Zeng, Manling Li, Tuan Lai, Heng Ji, Mohit Bansal, Hanghang Tong
Current methods for event representation ignore related events in a corpus-level global context.
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 • 6 May 2022 • Beidi Zhao, Boxin Du, Zhe Xu, Liangyue Li, Hanghang Tong
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input.
no code implementations • 5 May 2022 • Zhenning Zhang, Boxin Du, Hanghang Tong
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users.
no code implementations • 21 Apr 2022 • Senrong Xu, Yuan YAO, Liangyue Li, Wei Yang, Feng Xu, Hanghang Tong
In this work, we study the victim node detection problem under topology attacks against GNNs.
1 code implementation • 9 Mar 2022 • Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed for numerous issues, such as political agendas and manipulating financial markets.
no code implementations • 28 Feb 2022 • Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
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 • 14 Feb 2022 • Shengyu Feng, Baoyu Jing, Yada Zhu, Hanghang Tong
Contrastive learning is an effective unsupervised method in graph representation learning.
1 code implementation • 22 Nov 2021 • Tong Wang, Yuan YAO, Feng Xu, Shengwei An, Hanghang Tong, Ting Wang
We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain.
no code implementations • 28 Oct 2021 • Bolian Li, Baoyu Jing, Hanghang Tong
We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are semantically similar.
no code implementations • 16 Oct 2021 • Haonan Wang, Wei Huang, Andrew Margenot, Hanghang Tong, Jingrui He
Active learning theories and methods have been extensively studied in classical statistical learning settings.
no code implementations • 8 Oct 2021 • Wei Du, Xintao Wu, Hanghang Tong
However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions.
1 code implementation • 1 Oct 2021 • Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong, Tarek Abdelzaher
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
no code implementations • 8 Sep 2021 • Baoyu Jing, Yuejia Xiang, Xi Chen, Yu Chen, Hanghang Tong
To address these challenges, we propose a novel Graph Multi-View Prototypical (Graph-MVP) framework to extract node embeddings on multiplex graphs.
no code implementations • EMNLP 2021 • Baoyu Jing, Zeyu You, Tao Yang, Wei Fan, Hanghang Tong
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary.
1 code implementation • NAACL 2021 • Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, Dan Roth
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
no code implementations • 24 May 2021 • Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong
Algorithmic fairness is becoming increasingly important in data mining and machine learning, and one of the most fundamental notions is group fairness.
no code implementations • 23 May 2021 • Boxin Du, Changhe Yuan, Robert Barton, Tal Neiman, Hanghang Tong
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.
no code implementations • 19 May 2021 • Zhe Xu, Boxin Du, Hanghang Tong
Generally speaking, the vast majority of the existing works aim to answer the following question, that is, given a graph, what is the best way to mine it?
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 • 15 Feb 2021 • Baoyu Jing, Chanyoung Park, Hanghang Tong
To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way.
1 code implementation • 15 Feb 2021 • Baoyu Jing, Hanghang Tong, Yada Zhu
We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN).
1 code implementation • NeurIPS 2020 • Long Chen, Yuan YAO, Feng Xu, Miao Xu, Hanghang Tong
Collaborative filtering has been widely used in recommender systems.
no code implementations • 6 Nov 2020 • Lihui Liu, Boxin Du, Heng Ji, Hanghang Tong
In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
no code implementations • 10 Oct 2020 • Wei Du, Depeng Xu, Xintao Wu, Hanghang Tong
In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution.
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.
1 code implementation • 10 Jun 2020 • Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, Duen Horng Chau
By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field.
no code implementations • NeurIPS 2019 • Rui Zhang, Hanghang Tong
More significantly, the degree of the sparsity is steerable such that only exact k well-fitting samples with least reconstruction errors are activated during the optimization, while the residual samples, i. e., the extreme noised ones are eliminated for the global robustness.
no code implementations • NeurIPS 2019 • Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions.
no code implementations • 29 Jul 2019 • Zhuochen Jin, Nan Cao, Yang Shi, Hanghang Tong, Yingcai Wu
A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time.
no code implementations • 14 May 2019 • Rui Zhang, Luca Giancardo, Danilo A. Pena, Yejin Kim, Hanghang Tong, Xiaoqian Jiang
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
no code implementations • 14 May 2019 • Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
2 code implementations • 10 Apr 2015 • Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong, Yu Jing, Juanzi Li
The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.
Social and Information Networks
no code implementations • 3 Apr 2015 • Liangyue Li, Hanghang Tong
Understanding the dynamic mechanisms that drive the high-impact scientific work (e. g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources.
no code implementations • 3 Sep 2014 • Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng
In this paper, we propose a scalable, provable structured low-rank matrix factorization method to recover low-rank and sparse matrices from missing and grossly corrupted data, i. e., robust matrix completion (RMC) problems, or incomplete and grossly corrupted measurements, i. e., compressive principal component pursuit (CPCP) problems.
no code implementations • 18 Apr 2014 • Leman Akoglu, Hanghang Tong, Danai Koutra
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs.
Social and Information Networks Cryptography and Security
no code implementations • 27 Nov 2013 • Yuan Yao, Hanghang Tong, Tao Xie, Leman Akoglu, Feng Xu, Jian Lu
Community Question Answering (CQA) websites have become valuable repositories which host a massive volume of human knowledge.
no code implementations • NeurIPS 2012 • Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw Szymanski
In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples.