Search Results for author: Hanghang Tong

Found 41 papers, 10 papers with code

EventKE: Event-Enhanced Knowledge Graph Embedding

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.

Knowledge Graph Embedding Knowledge Graphs

HIT: Nested Named Entity Recognition via Head-Tail Pair and Token Interaction

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.

NER Nested Named Entity Recognition

Trustworthy Graph Neural Networks: Aspects, Methods and Trends

no code implementations16 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.

Drug Discovery Edge-computing +4

Learning Optimal Propagation for Graph Neural Networks

no code implementations6 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.

Node Classification

SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation

no code implementations5 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.

Recommendation Systems

Detecting Topology Attacks against Graph Neural Networks

no code implementations21 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.

Node Classification

DISCO: Comprehensive and Explainable Disinformation Detection

1 code implementation9 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.

Fake News Detection

RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network

no code implementations28 Feb 2022 Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong

Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.

Data Augmentation for Deep Graph Learning: A Survey

no code implementations16 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.

Data Augmentation Graph Learning

Backdoor Attack through Frequency Domain

1 code implementation22 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.

Autonomous Driving Backdoor Attack

Graph Communal Contrastive Learning

no code implementations28 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.

Community Detection Contrastive Learning +1

Deep Active Learning by Leveraging Training Dynamics

no code implementations16 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.

Active Learning

Fair Regression under Sample Selection Bias

no code implementations8 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.

Fairness Selection bias

Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders

1 code implementation1 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.

Representation Learning Stance Detection

Graph-MVP: Multi-View Prototypical Contrastive Learning for Multiplex Graphs

no code implementations8 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.

Contrastive Learning Graph Representation Learning +1

Event Time Extraction and Propagation via Graph Attention Networks

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.

Graph Attention Natural Language Understanding +1

MultiFair: Multi-Group Fairness in Machine Learning

no code implementations24 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.


Hypergraph Pre-training with Graph Neural Networks

no code implementations23 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.

hyperedge classification Representation Learning

Graph Sanitation with Application to Node Classification

no code implementations19 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?

Anomaly Detection Bilevel Optimization +5

Few-shot Network Anomaly Detection via Cross-network Meta-learning

no code implementations22 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.

Anomaly Detection Few-Shot Learning

HDMI: High-order Deep Multiplex Infomax

1 code implementation15 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.

Node Classification Representation Learning

Network of Tensor Time Series

1 code implementation15 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).

Tensor Decomposition Time Series

KompaRe: A Knowledge Graph Comparative Reasoning System

no code implementations6 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.

Knowledge Graphs

Fairness-aware Agnostic Federated Learning

no code implementations10 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.

Fairness Federated Learning

Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining

no code implementations17 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.

Representation Learning

Evaluating Graph Vulnerability and Robustness using TIGER

1 code implementation10 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.

Robust Principal Component Analysis with Adaptive Neighbors

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.

PC-Fairness: A Unified Framework for Measuring Causality-based Fairness

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.


EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data

no code implementations29 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.

From Brain Imaging to Graph Analysis: a study on ADNI's patient cohort

no code implementations14 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).

feature selection General Classification +1

Visual Analytics of Anomalous User Behaviors: A Survey

no code implementations14 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.

Anomaly Detection

Panther: Fast Top-k Similarity Search in Large Networks

2 code implementations10 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

The Child is Father of the Man: Foresee the Success at the Early Stage

no code implementations3 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.

Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations

no code implementations3 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.

Matrix Completion

Graph-based Anomaly Detection and Description: A Survey

no code implementations18 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

Want a Good Answer? Ask a Good Question First!

no code implementations27 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.

Community Question Answering

GenDeR: A Generic Diversified Ranking Algorithm

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.

Information Retrieval

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