Search Results for author: Hanghang Tong

Found 70 papers, 21 papers with code

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.

named-entity-recognition Named Entity Recognition +2

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 +1

Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning

1 code implementation11 Sep 2023 Yunyong Ko, Hanghang Tong, Sang-Wook Kim

To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (CASH) that employs (1) context-aware node aggregation to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2).

Contrastive Learning Hyperedge Prediction +2

Ensuring User-side Fairness in Dynamic Recommender Systems

no code implementations29 Aug 2023 Hyunsik Yoo, Zhichen Zeng, Jian Kang, Zhining Liu, David Zhou, Fei Wang, Eunice Chan, Hanghang Tong

However, fairness-constrained re-ranking, a typical method to ensure user-side fairness (i. e., reducing performance disparity), faces two fundamental challenges in the dynamic setting: (1) non-differentiability of the ranking-based fairness constraint, which hinders the end-to-end training paradigm, and (2) time-inefficiency, which impedes quick adaptation to changes in user preferences.

Fairness Recommendation Systems +1

Topological Augmentation for Class-Imbalanced Node Classification

no code implementations27 Aug 2023 Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong

Class imbalance is prevalent in real-world node classification tasks and often biases graph learning models toward majority classes.

Classification Graph Learning +1

Noisy Positive-Unlabeled Learning with Self-Training for Speculative Knowledge Graph Reasoning

no code implementations13 Jun 2023 Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, Tarek F. Abdelzaher

State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.

Knowledge Graphs

BeMap: Balanced Message Passing for Fair Graph Neural Network

no code implementations7 Jun 2023 Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong

Graph Neural Network (GNN) has shown strong empirical performance in many downstream tasks by iteratively aggregating information from the local neighborhood of each node, i. e., message passing.

Fairness Node Classification

Reconstructing Graph Diffusion History from a Single Snapshot

1 code implementation1 Jun 2023 Ruizhong Qiu, Dingsu Wang, Lei Ying, H. Vincent Poor, Yifang Zhang, Hanghang Tong

They are exclusively based on the maximum likelihood estimation (MLE) formulation and require to know true diffusion parameters.

Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders

no code implementations29 May 2023 Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew J Margenot, Hanghang Tong

First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.

Imputation Inductive Bias +2

Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning

1 code implementation22 May 2023 Chi Han, Qizheng He, Charles Yu, Xinya Du, Hanghang Tong, Heng Ji

A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph.

Link Prediction

Neural Multi-network Diffusion towards Social Recommendation

no code implementations11 Apr 2023 Boxin Du, Lihui Liu, Jiejun Xu, Fei Wang, Hanghang Tong

Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation.

FairGen: Towards Fair Graph Generation

no code implementations30 Mar 2023 Lecheng Zheng, Dawei Zhou, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He

In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.

Data Augmentation Fairness +3

KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction

1 code implementation23 Feb 2023 Yunyong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, JaeHoon Kim, SoHyun Park, Kyungsik Han, Hanghang Tong, Sang-Wook Kim

Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance.

Knowledge Graphs

Do We Really Need Complicated Model Architectures For Temporal Networks?

no code implementations22 Feb 2023 Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi

Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.

Graph Learning Link Prediction

Concept Discovery for Fast Adapatation

no code implementations19 Jan 2023 Shengyu Feng, Hanghang Tong

The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickly adapt to a new task.

Few-Shot Learning

GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network

no code implementations8 Nov 2022 Chuxuan Hu, Qinghai Zhou, Hanghang Tong

Subteam replacement is defined as finding the optimal candidate set of people who can best function as an unavailable subset of members (i. e., subteam) for certain reasons (e. g., conflicts of interests, employee churn), given a team of people embedded in a social network working on the same task.

Clustering Representation Learning

JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks

no code implementations12 Oct 2022 Jian Kang, Qinghai Zhou, Hanghang Tong

The proposed JuryGCN is capable of quantifying uncertainty deterministically without modifying the GCN architecture or introducing additional parameters.

Active Learning Graph Mining +1

Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs

no code implementations4 Oct 2022 Haipeng Luo, Hanghang Tong, Mengxiao Zhang, Yuheng Zhang

For general strongly observable graphs, we develop an algorithm that achieves the optimal regret $\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$ with high probability, where $\alpha_t$ is the independence number of the feedback graph at round $t$.

Multi-Armed Bandits

Improved Algorithms for Neural Active Learning

1 code implementation2 Oct 2022 Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He

We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.

Active Learning

Solving Coupled Differential Equation Groups Using PINO-CDE

1 code implementation1 Oct 2022 Wenhao Ding, Qing He, Hanghang Tong, Qingjing Wang, Ping Wang

This framework integrates engineering dynamics and deep learning technologies and may reveal a new concept for CDEs solving and uncertainty propagation.

Retrieval Based Time Series Forecasting

no code implementations27 Sep 2022 Baoyu Jing, Si Zhang, Yada Zhu, Bin Peng, Kaiyu Guan, Andrew Margenot, Hanghang Tong

In this paper, we show both theoretically and empirically that the uncertainty could be effectively reduced by retrieving relevant time series as references.

Imputation Retrieval +2

ARIEL: Adversarial Graph Contrastive Learning

no code implementations15 Aug 2022 Shengyu Feng, Baoyu Jing, Yada Zhu, Hanghang Tong

In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within reasonable constraints.

Contrastive Learning Data Augmentation +1

Privacy-preserving Graph Analytics: Secure Generation and Federated Learning

no code implementations30 Jun 2022 Dongqi Fu, Jingrui He, Hanghang Tong, Ross Maciejewski

Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.

Federated Learning Graph Generation +2

Geometric Matrix Completion via Sylvester Multi-Graph Neural Network

no code implementations19 Jun 2022 Boxin Du, Changhe Yuan, Fei Wang, Hanghang Tong

Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations.

Graph Mining Matrix Completion

Schema-Guided Event Graph Completion

no code implementations6 Jun 2022 Hongwei Wang, Zixuan Zhang, Sha Li, Jiawei Han, Yizhou Sun, Hanghang Tong, Joseph P. Olive, Heng Ji

Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.

Link Prediction

CoNSoLe: Convex Neural Symbolic Learning

no code implementations1 Jun 2022 Haoran Li, Yang Weng, Hanghang Tong

In the first step of searching for right symbols, we convexify the deep Q-learning.


COIN: Co-Cluster Infomax for Bipartite Graphs

no code implementations31 May 2022 Baoyu Jing, Yuchen Yan, Yada Zhu, Hanghang Tong

We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes.

Drug Discovery Information Retrieval +3

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

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 in 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

1 code implementation16 Feb 2022 Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu

In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems.

Data Augmentation Graph Learning

IMBENS: Ensemble Class-imbalanced Learning in Python

1 code implementation24 Nov 2021 Zhining Liu, Jian Kang, Hanghang Tong, Yi Chang

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem.

Ensemble 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

1 code implementation28 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, Ziwei Wu, 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 regression +1

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

X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning

no code implementations8 Sep 2021 Baoyu Jing, Shengyu Feng, Yuejia Xiang, Xi Chen, Yu Chen, Hanghang Tong

X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings.

Contrastive Learning Graph Learning +2

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 +2

InfoFair: Information-Theoretic Intersectional Fairness

no code implementations24 May 2021 Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong

The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e. g., gender, race, marital status, etc.)

BIG-bench Machine Learning 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 +1

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 +6

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

2 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 +1

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 +1

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

1 code implementation18 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 Retrieval

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