Search Results for author: Huan Liu

Found 132 papers, 54 papers with code

STANCE-C3: Domain-adaptive Cross-target Stance Detection via Contrastive Learning and Counterfactual Generation

no code implementations26 Sep 2023 Nayoung Kim, David Mosallanezhad, Lu Cheng, Michelle V. Mancenido, Huan Liu

We also propose a modified self-supervised contrastive learning as a component of STANCE-C3 to prevent overfitting for the existing domain and target and enable cross-target stance detection.

Contrastive Learning Data Augmentation +1

Disinformation Detection: An Evolving Challenge in the Age of LLMs

no code implementations25 Sep 2023 Bohan Jiang, Zhen Tan, Ayushi Nirmal, Huan Liu

A holistic exploration for the formation and detection of disinformation is conducted to foster this line of research.

LLMs as Counterfactual Explanation Modules: Can ChatGPT Explain Black-box Text Classifiers?

no code implementations23 Sep 2023 Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, Huan Liu

Large language models (LLMs) are increasingly being used for tasks beyond text generation, including complex tasks such as data labeling, information extraction, etc.

Counterfactual Explanation Specificity +3

Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation

no code implementations18 Sep 2023 Huan Liu, Zichang Tan, Qiang Chen, Yunchao Wei, Yao Zhao, Jingdong Wang

Moreover, to address the semantic conflicts between image and frequency domains, the forgery-aware mutual module is developed to further enable the effective interaction of disparate image and frequency features, resulting in aligned and comprehensive visual forgery representations.


ConDA: Contrastive Domain Adaptation for AI-generated Text Detection

1 code implementation7 Sep 2023 Amrita Bhattacharjee, Tharindu Kumarage, Raha Moraffah, Huan Liu

Given the potential malicious nature in which these LLMs can be used to generate disinformation at scale, it is important to build effective detectors for such AI-generated text.

Contrastive Learning Text Detection +1

UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment

no code implementations20 Aug 2023 Yougang Xiao, Hao Yang, Huan Liu, Keyu Wu, Guohua Wu

Unmanned aerial vehicles (UAVs) are desirable platforms for time-efficient and cost-effective task execution.

Decision Making

Neural Authorship Attribution: Stylometric Analysis on Large Language Models

no code implementations14 Aug 2023 Tharindu Kumarage, Huan Liu

Large language models (LLMs) such as GPT-4, PaLM, and Llama have significantly propelled the generation of AI-crafted text.

Language Modelling Misinformation

Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation

2 code implementations ICCV 2023 Huan Liu, Qiang Chen, Zichang Tan, Jiang-Jiang Liu, Jian Wang, Xiangbo Su, Xiaolong Li, Kun Yao, Junyu Han, Errui Ding, Yao Zhao, Jingdong Wang

State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e. g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR.

Human Detection Multi-Person Pose Estimation

Causality Guided Disentanglement for Cross-Platform Hate Speech Detection

1 code implementation3 Aug 2023 Paras Sheth, Tharindu Kumarage, Raha Moraffah, Aman Chadha, Huan Liu

By disentangling input into platform-dependent features (useful for predicting hate targets) and platform-independent features (used to predict the presence of hate), we learn invariant representations resistant to distribution shifts.

Disentanglement Hate Speech Detection

Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?

1 code implementation2 Aug 2023 Amrita Bhattacharjee, Huan Liu

Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale.

Text Detection

UPREVE: An End-to-End Causal Discovery Benchmarking System

no code implementations25 Jul 2023 Suraj Jyothi Unni, Paras Sheth, Kaize Ding, Huan Liu, K. Selcuk Candan

Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making.

Benchmarking Causal Discovery +1

FDCT: Fast Depth Completion for Transparent Objects

1 code implementation23 Jul 2023 Tianan Li, Zhehan Chen, Huan Liu, Chen Wang

To address these challenges, we propose a Fast Depth Completion framework for Transparent objects (FDCT), which also benefits downstream tasks like object pose estimation.

Autonomous Driving Depth Completion +3

Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach

1 code implementation10 Jul 2023 Faisal Alatawi, Paras Sheth, Huan Liu

To facilitate measuring distances between users, we propose EchoGAE, a self-supervised graph autoencoder-based user embedding model that leverages users' posts and the interaction graph to embed them in a manner that reflects their ideological similarity.

Contrastive Meta-Learning for Few-shot Node Classification

1 code implementation27 Jun 2023 Song Wang, Zhen Tan, Huan Liu, Jundong Li

First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes.

Classification Graph Mining +2

PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework

1 code implementation15 Jun 2023 Paras Sheth, Tharindu Kumarage, Raha Moraffah, Aman Chadha, Huan Liu

Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or other characteristics.

Hate Speech Detection

Inductive Linear Probing for Few-shot Node Classification

no code implementations14 Jun 2023 Hirthik Mathavan, Zhen Tan, Nivedh Mudiam, Huan Liu

Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting.

Classification Few-Shot Learning +1

Virtual Node Tuning for Few-shot Node Classification

no code implementations9 Jun 2023 Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu

Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task.

Classification Graph Representation Learning +2

Learning Strong Graph Neural Networks with Weak Information

1 code implementation29 May 2023 Yixin Liu, Kaize Ding, Jianling Wang, Vincent Lee, Huan Liu, Shirui Pan

Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.

Graph Learning

SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge

no code implementations25 Apr 2023 Ke Chen, Liangyan Li, Huan Liu, Yunzhe Li, Congling Tang, Jun Chen

Stereo Image Super-Resolution (stereoSR) has attracted significant attention in recent years due to the extensive deployment of dual cameras in mobile phones, autonomous vehicles and robots.

Autonomous Vehicles Image Restoration +1

A Data-Centric Solution to NonHomogeneous Dehazing via Vision Transformer

1 code implementation16 Apr 2023 Yangyi Liu, Huan Liu, Liangyan Li, Zijun Wu, Jun Chen

Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing approach that reduces the distribution gaps between the target dataset and the augmented one.

Image Dehazing

Fairness through Aleatoric Uncertainty

1 code implementation7 Apr 2023 Anique Tahir, Lu Cheng, Huan Liu

We then propose a principled model to improve fairness when aleatoric uncertainty is high and improve utility elsewhere.


Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank

1 code implementation CVPR 2023 Shirui Huang, Keyan Wang, Huan Liu, Jun Chen, Yunsong Li

Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress.

Underwater Image Restoration

Stylometric Detection of AI-Generated Text in Twitter Timelines

no code implementations7 Mar 2023 Tharindu Kumarage, Joshua Garland, Amrita Bhattacharjee, Kirill Trapeznikov, Scott Ruston, Huan Liu

However, tweets are inherently short, thus making it difficult for current state-of-the-art pre-trained language model-based detectors to accurately detect at what point the AI starts to generate tweets in a given Twitter timeline.

Language Modelling Misinformation

Towards Detecting Harmful Agendas in News Articles

1 code implementation31 Jan 2023 Melanie Subbiah, Amrita Bhattacharjee, Yilun Hua, Tharindu Kumarage, Huan Liu, Kathleen McKeown

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread.


Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks

1 code implementation24 Dec 2022 Ujun Jeong, Kaize Ding, Lu Cheng, Ruocheng Guo, Kai Shu, Huan Liu

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society.

Fake News Detection

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

1 code implementation11 Dec 2022 Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu

More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed.

Classification Contrastive Learning +4

Distributional Shift Adaptation using Domain-Specific Features

1 code implementation9 Nov 2022 Anique Tahir, Lu Cheng, Ruocheng Guo, Huan Liu

Machine learning algorithms typically assume that the training and test samples come from the same distributions, i. e., in-distribution.

GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection

1 code implementation8 Nov 2022 Yixin Liu, Kaize Ding, Huan Liu, Shirui Pan

As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.

Contrastive Learning Data Augmentation +2

Debiasing Word Embeddings with Nonlinear Geometry

1 code implementation COLING 2022 Lu Cheng, Nayoung Kim, Huan Liu

Therefore, this work studies biases associated with multiple social categories: joint biases induced by the union of different categories and intersectional biases that do not overlap with the biases of the constituent categories.

Word Embeddings

Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity

no code implementations26 Aug 2022 Kaize Ding, Elnaz Nouri, Guoqing Zheng, Huan Liu, Ryen White

The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice.

Data Augmentation Node Classification

Trustworthy Recommender Systems

no code implementations10 Aug 2022 Shoujin Wang, Xiuzhen Zhang, Yan Wang, Huan Liu, Francesco Ricci

However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs.

Recommendation Systems

Estimating Topic Exposure for Under-Represented Users on Social Media

no code implementations7 Aug 2022 Mansooreh Karami, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu

To reduce the bias induced by the contributors, in this work, we focus on highlighting the engagers' contributions in the observed data as they are more likely to contribute when compared to lurkers, and they comprise a bigger population as compared to the contributors.

CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine Hesitancy Research

1 code implementation30 Jun 2022 Bohan Jiang, Paras Sheth, Baoxin Li, Huan Liu

Despite the astonishing success of COVID-19 vaccines against the virus, a substantial proportion of the population is still hesitant to be vaccinated, undermining governmental efforts to control the virus.


Toward Understanding Bias Correlations for Mitigation in NLP

no code implementations24 May 2022 Lu Cheng, Suyu Ge, Huan Liu

In particular, we examine bias mitigation in two common NLP tasks -- toxicity detection and word embeddings -- on three social identities, i. e., race, gender, and religion.

Fairness Word Embeddings

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution

Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo

1 code implementation6 May 2022 Qiang Sheng, Juan Cao, H. Russell Bernard, Kai Shu, Jintao Li, Huan Liu

False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world.

Causal Disentanglement with Network Information for Debiased Recommendations

no code implementations14 Apr 2022 Paras Sheth, Ruocheng Guo, Lu Cheng, Huan Liu, K. Selçuk Candan

Aside from the user conformity, aspects of confounding such as item popularity present in the network information is also captured in our method with the aid of \textit{causal disentanglement} which unravels the learned representations into independent factors that are responsible for (a) modeling the exposure of an item to the user, (b) predicting the ratings, and (c) controlling the hidden confounders.

Causal Inference Disentanglement +1

Logistics in the Sky: A Two-phase Optimization Approach for the Drone Package Pickup and Delivery System

no code implementations4 Apr 2022 Fangyu Hong, Guohua Wu, Qizhang Luo, Huan Liu, Xiaoping Fang, Witold Pedrycz

Different from the previous urban distribution mode that depends on trucks, this paper proposes a novel package pick-up and delivery mode and system in which multiple drones collaborate with automatic devices.


Supervised Graph Contrastive Learning for Few-shot Node Classification

no code implementations29 Mar 2022 Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu

Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis.

Classification Contrastive Learning +4

Few-Shot Learning on Graphs

no code implementations17 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.

Few-Shot Learning Graph Mining +1

Deep Graph Learning for Anomalous Citation Detection

no code implementations23 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.

Anomaly Detection Graph Learning +1

Physics-Informed Graph Learning

no code implementations22 Feb 2022 Ciyuan Peng, Feng Xia, Vidya Saikrishna, Huan Liu

The graph learning models suffer from the inability to efficiently learn graph information.

Graph Learning

Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning

1 code implementation17 Feb 2022 Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu

In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods.

Contrastive Learning Representation Learning

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

Domain Adaptive Fake News Detection via Reinforcement Learning

no code implementations16 Feb 2022 Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu, Michelle V. Mancenido, Huan Liu

With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news.

Fake News Detection reinforcement-learning +1

"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders

no code implementations9 Feb 2022 Xinyi Zhou, Kai Shu, Vir V. Phoha, Huan Liu, Reza Zafarani

To distinguish between intentional versus unintentional spreading, we study the psychological explanations of unintentional spreading.

Evaluation Methods and Measures for Causal Learning Algorithms

no code implementations7 Feb 2022 Lu Cheng, Ruocheng Guo, Raha Moraffah, Paras Sheth, K. Selcuk Candan, Huan Liu

To bridge from conventional causal inference (i. e., based on statistical methods) to causal learning with big data (i. e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning.

Benchmarking BIG-bench Machine Learning +1

MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning

no code implementations CVPR 2022 Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, Jin Tang

The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i. e. incrementally learning new classes) during testing.

class-incremental learning Few-Shot Class-Incremental Learning +2

Towards Multi-Domain Single Image Dehazing via Test-Time Training

no code implementations CVPR 2022 Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, Keyan Wang

Motivated by this observation, we propose a test-time training method which leverages a helper network to assist the dehazing model in better adapting to a domain of interest.

Image Dehazing Meta-Learning +1

Graph Few-shot Class-incremental Learning

1 code implementation23 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.

class-incremental learning Few-Shot Class-Incremental Learning +3

Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies

1 code implementation19 Dec 2021 Lu Cheng, Ruocheng Guo, Huan Liu

This work empirically examines the causal effects of user-generated online reviews on a granular level: we consider multiple aspects, e. g., the Food and Service of a restaurant.

Causal Inference

Meta Propagation Networks for Graph Few-shot Semi-supervised Learning

1 code implementation18 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.

Graph Learning Meta-Learning

A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation

no code implementations9 Dec 2021 Faisal Alatawi, Lu Cheng, Anique Tahir, Mansooreh Karami, Bohan Jiang, Tyler Black, Huan Liu

These mechanisms could be manifested in two forms: (1) the bias of social media's recommender systems and (2) internal biases such as confirmation bias and homophily.

Misinformation Recommendation Systems

A Novel Sequence Tagging Framework for Consumer Event-Cause Extraction

no code implementations28 Oct 2021 Congqing He, Jie Zhang, Xiangyu Zhu, Huan Liu, Yukun Huang

To this end, we introduce a fresh perspective to revisit the relational event-cause extraction task and propose a novel sequence tagging framework, instead of extracting event types and events-causes separately.

Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network

no code implementations22 Oct 2021 Huan Liu, Junsong Yuan, Chen Wang, Jun Chen

Despite recent improvement of supervised monocular depth estimation, the lack of high quality pixel-wise ground truth annotations has become a major hurdle for further progress.

Knowledge Distillation Monocular Depth Estimation +1

Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication

1 code implementation4 Oct 2021 Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan, Huan Liu

Online review systems are the primary means through which many businesses seek to build the brand and spread their messages.

Causal Inference

Cross-Domain Lossy Compression as Optimal Transport with an Entropy Bottleneck

no code implementations ICLR 2022 Huan Liu, George Zhang, Jun Chen, Ashish J Khisti

We study the problem of cross-domain lossy compression where the reconstruction distribution is different from the source distribution in order to account for distributional shift due to processing.

Denoising Super-Resolution

Learning to Selectively Learn for Weakly-supervised Paraphrase Generation

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.

Language Modelling Meta-Learning +2

Semantics-Guided Contrastive Network for Zero-Shot Object detection

no code implementations4 Sep 2021 Caixia Yan, Xiaojun Chang, Minnan Luo, Huan Liu, Xiaoqin Zhang, Qinghua Zheng

To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection.

Contrastive Learning Generalized Zero-Shot Object Detection +2

NI-UDA: Graph Adversarial Domain Adaptation from Non-shared-and-Imbalanced Big Data to Small Imbalanced Applications

no code implementations11 Aug 2021 Guangyi Xiao, Weiwei Xiang, Huan Liu, Hao Chen, Shun Peng, Jingzhi Guo, Zhiguo Gong

We propose a new general Graph Adversarial Domain Adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptation (UDA) from the big data with non-shared and imbalanced classes to specified small and imbalanced applications (NI-UDA), where non-shared classes mean the label space out of the target domain.

Unsupervised Domain Adaptation

Mitigating Bias in Session-based Cyberbullying Detection: A Non-Compromising Approach

1 code implementation ACL 2021 Lu Cheng, Ahmadreza Mosallanezhad, Yasin Silva, Deborah Hall, Huan Liu

The element of repetition in cyberbullying behavior has directed recent computational studies toward detecting cyberbullying based on a social media session.

Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification

no code implementations12 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.

Classification Graph Learning +3

Graph Learning: A Survey

no code implementations3 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.

BIG-bench Machine Learning Combinatorial Optimization +3

Causal Learning for Socially Responsible AI

no code implementations25 Apr 2021 Lu Cheng, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu

The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.


DW-GAN: A Discrete Wavelet Transform GAN for NonHomogeneous Dehazing

1 code implementation18 Apr 2021 Minghan Fu, Huan Liu, Yankun Yu, Jun Chen, Keyan Wang

By utilizing wavelet transform in DWT branch, our proposed method can retain more high-frequency knowledge in feature maps.

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

Causal Mediation Analysis with Hidden Confounders

no code implementations21 Feb 2021 Lu Cheng, Ruocheng Guo, Huan Liu

An important problem in causal inference is to break down the total effect of a treatment on an outcome into different causal pathways and to quantify the causal effect in each pathway.

Causal Inference Fairness

Causal Inference for Time series Analysis: Problems, Methods and Evaluation

no code implementations11 Feb 2021 Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, Huan Liu

In this paper, we focus on two causal inference tasks, i. e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task.

Causal Discovery Causal Inference +3

Indirect Domain Shift for Single Image Dehazing

no code implementations5 Feb 2021 Huan Liu, Jun Chen

Therefore, it is capable of consolidating the expressibility of different architectures, resulting in a more accurate indirect domain shift (IDS) from the hazy images to that of clear images.

Image Dehazing Single Image Dehazing

Socially Responsible AI Algorithms: Issues, Purposes, and Challenges

no code implementations1 Jan 2021 Lu Cheng, Kush R. Varshney, Huan Liu

In this survey, we provide a systematic framework of Socially Responsible AI Algorithms that aims to examine the subjects of AI indifference and the need for socially responsible AI algorithms, define the objectives, and introduce the means by which we may achieve these objectives.


"Let's Eat Grandma": Does Punctuation Matter in Sentence Representation?

1 code implementation10 Dec 2020 Mansooreh Karami, Ahmadreza Mosallanezhad, Michelle V Mancenido, Huan Liu

Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture lexical and semantic similarities and dissimilarities.

Sentiment Analysis Sentiment Classification

Fact-Enhanced Synthetic News Generation

1 code implementation8 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.

News Generation Text Summarization +1

MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation

2 code implementations8 Nov 2020 Yichuan Li, Bohan Jiang, Kai Shu, Huan Liu

The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two.

Social and Information Networks Computers and Society

Improving Cyberbully Detection with User Interaction

1 code implementation1 Nov 2020 Suyu Ge, Lu Cheng, Huan Liu

Cyberbullying, identified as intended and repeated online bullying behavior, has become increasingly prevalent in the past few decades.

Disinformation in the Online Information Ecosystem: Detection, Mitigation and Challenges

no code implementations18 Oct 2020 Amrita Bhattacharjee, Kai Shu, Min Gao, Huan Liu

We then proceed to discuss the inherent challenges in disinformation research, and then elaborate on the computational and interdisciplinary approaches towards mitigation of disinformation, after a short overview of the various directions explored in detection efforts.


Toward Privacy and Utility Preserving Image Representation

no code implementations30 Sep 2020 Ahmadreza Mosallanezhad, Yasin N. Silva, Michelle V. Mancenido, Huan Liu

Face images are rich data items that are useful and can easily be collected in many applications, such as in 1-to-1 face verification tasks in the domain of security and surveillance systems.

Face Verification Privacy Preserving

Long-Term Effect Estimation with Surrogate Representation

1 code implementation19 Aug 2020 Lu Cheng, Ruocheng Guo, Huan Liu

Second, short-term outcomes are often directly used as the proxy of the primary outcome, i. e., the surrogate.

Causal Inference

Combating Disinformation in a Social Media Age

no code implementations14 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.

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

1 code implementation23 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.

Classification Drug Discovery +5

Adversarial Attacks and Defenses: An Interpretation Perspective

no code implementations23 Apr 2020 Ninghao Liu, Mengnan Du, Ruocheng Guo, Huan Liu, Xia Hu

In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation.

Adversarial Attack Adversarial Defense +2

Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis

no code implementations4 Jan 2020 Ghazaleh Beigi, Jiliang Tang, Huan Liu

The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks, and mandates dedicated efforts on link analysis for signed social networks.

Feature Engineering Link Prediction +1

Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements

1 code implementation2 Jan 2020 Kai Shu, Suhang Wang, Dongwon Lee, Huan Liu

In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media.

Ethics Fact Checking

Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

no code implementations22 Dec 2019 Ruocheng Guo, Jundong Li, Huan Liu

When such data comes with network information, the later can be potentially useful to correct hidden confounding bias.

Causal Inference Recommendation Systems

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

no code implementations22 Nov 2019 Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo, Hamidreza Alvari, Alexander Nou, Huan Liu

The attacker seeks to infer users' private-attribute information according to their items list and recommendations.

Deep causal representation learning for unsupervised domain adaptation

no code implementations28 Oct 2019 Raha Moraffah, Kai Shu, Adrienne Raglin, Huan Liu

Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains.

Representation Learning Unsupervised Domain Adaptation

Detecting Fake News with Weak Social Supervision

no code implementations24 Oct 2019 Kai Shu, Ahmed Hassan Awadallah, Susan Dumais, Huan Liu

This is especially the case for many real-world tasks where large scale annotated examples are either too expensive to acquire or unavailable due to privacy or data access constraints.

Fake News Detection

Feature Interaction-aware Graph Neural Networks

no code implementations19 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.

Graph Learning Representation Learning

Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals

2 code implementations31 Jul 2019 Xiang Zhang, Xiaocong Chen, Manqing Dong, Huan Liu, Chang Ge, Lina Yao

In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.

EEG Electroencephalogram (EEG) +1

Learning Individual Causal Effects from Networked Observational Data

1 code implementation8 Jun 2019 Ruocheng Guo, Jundong Li, Huan Liu

In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders.

Causal Inference

Deep Anomaly Detection on Attributed Networks

2 code implementations 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.

Anomaly Detection

Applications of Social Media in Hydroinformatics: A Survey

no code implementations1 May 2019 Yu-Feng Yu, Yuelong Zhu, Dingsheng Wan, Qun Zhao, Kai Shu, Huan Liu

Floods of research and practical applications employ social media data for a wide range of public applications, including environmental monitoring, water resource managing, disaster and emergency response. Hydroinformatics can benefit from the social media technologies with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper first proposes a 4W (What, Why, When, hoW) model and a methodological structure to better understand and represent the application of social media to hydroinformatics, then provides an overview of academic research of applying social media to hydroinformatics such as water environment, water resources, flood, drought and water Scarcity management.

Fake News Detection Management

A Novel Trend Symbolic Aggregate Approximation for Time Series

no code implementations1 May 2019 Yu-Feng Yu, Yuelong Zhu, Dingsheng Wan, Qun Zhao, Huan Liu

The experimental results on diverse time series data sets demonstrate that our proposed representation significantly outperforms the original SAX representation and an improved SAX representation for classification.

General Classification Time Series +1

The Role of User Profile for Fake News Detection

no code implementations30 Apr 2019 Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, Huan Liu

In an attempt to understand connections between user profiles and fake news, first, we measure users' sharing behaviors on social media and group representative users who are more likely to share fake and real news; then, we perform a comparative analysis of explicit and implicit profile features between these user groups, which reveals their potential to help differentiate fake news from real news.

Fake News Detection Feature Importance +1

Hierarchical Propagation Networks for Fake News Detection: Investigation and Exploitation

2 code implementations21 Mar 2019 Kai Shu, Deepak Mahudeswaran, Suhang Wang, Huan Liu

In an attempt to understand the correlations between news propagation networks and fake news, first, we build a hierarchical propagation network from macro-level and micro-level of fake news and true news; second, we perform a comparative analysis of the propagation network features of linguistic, structural and temporal perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection.

Social and Information Networks

Signed Link Prediction with Sparse Data: The Role of Personality Information

no code implementations6 Mar 2019 Ghazaleh Beigi, Suhas Ranganath, Huan Liu

Predicting signed links in social networks often faces the problem of signed link data sparsity, i. e., only a small percentage of signed links are given.

Link Prediction

Graph Neural Networks for User Identity Linkage

no code implementations6 Mar 2019 Wen Zhang, Kai Shu, Huan Liu, Yalin Wang

In particular, we provide a principled approach to jointly capture local and global information in the user-user social graph and propose the framework {\m}, which jointly learning user representations for user identity linkage.

Online Newton Step Algorithm with Estimated Gradient

no code implementations25 Nov 2018 Binbin Liu, Jundong Li, Yunquan Song, Xijun Liang, Ling Jian, Huan Liu

In particular, we extend the ONS algorithm with the trick of expected gradient and develop a novel second-order online learning algorithm, i. e., Online Newton Step with Expected Gradient (ONSEG).

A Survey of Learning Causality with Data: Problems and Methods

3 code implementations25 Sep 2018 Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, Huan Liu

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations.

BIG-bench Machine Learning

FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media

6 code implementations5 Sep 2018 Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, Huan Liu

However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information.

Social and Information Networks

Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

2 code implementations ASONAM 2019 2019 Jundong Li, Liang Wu, Huan Liu

As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.

Ensemble Learning Feature Engineering +1

Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects

1 code implementation9 Aug 2018 Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, Huan Liu

Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research.

Variational Inference

Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection

3 code implementations13 Jun 2018 Guansong Pang, Longbing Cao, Ling Chen, Huan Liu

However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i. e., outliers).

Anomaly Detection Disease Prediction +3

Exploiting Tri-Relationship for Fake News Detection

4 code implementations20 Dec 2017 Kai Shu, Suhang Wang, Huan Liu

Recent Social and Psychology studies show potential importance to utilize social media data: 1) Confirmation bias effect reveals that consumers prefer to believe information that confirms their existing stances; 2) Echo chamber effect suggests that people tend to follow likeminded users and form segregated communities on social media.

Social and Information Networks

Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications

no code implementations14 Sep 2017 Fred Morstatter, Kai Shu, Suhang Wang, Huan Liu

We apply our solution to sentiment analysis, a task that can benefit from the emoji calibration technique we use in this work.

Sentiment Analysis

Fake News Detection on Social Media: A Data Mining Perspective

6 code implementations7 Aug 2017 Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, Huan Liu

First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.

Fake News Detection

Attributed Network Embedding for Learning in a Dynamic Environment

no code implementations6 Jun 2017 Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu

To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly.

Clustering Link Prediction +2

NeuroRule: A Connectionist Approach to Data Mining

no code implementations5 Jan 2017 Hongjun Lu, Rudy Setiono, Huan Liu

One of the major reasons cited is that knowledge generated by neural networks is not explicitly represented in the form of rules suitable for verification or interpretation by humans.

General Classification

Challenges of Feature Selection for Big Data Analytics

no code implementations7 Nov 2016 Jundong Li, Huan Liu

We are surrounded by huge amounts of large-scale high dimensional data.

feature selection

SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification

no code implementations17 Aug 2016 Liang Wu, Fred Morstatter, Huan Liu

To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content.

General Classification Sentiment Analysis +1

Feature Selection: A Data Perspective

2 code implementations29 Jan 2016 Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, Huan Liu

To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).

feature selection Sparse Learning

A Survey of Signed Network Mining in Social Media

no code implementations24 Nov 2015 Jiliang Tang, Yi Chang, Charu Aggarwal, Huan Liu

Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines.

Finding Eyewitness Tweets During Crises

no code implementations WS 2014 Fred Morstatter, Nichola Lubold, Heather Pon-Barry, Jürgen Pfeffer, Huan Liu

These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region.

Disaster Response

mTrust: Discerning Multi-Faceted Trust in a Connected World

no code implementations WSDM 2012 Jiliang Tang, Huiji Gao, Huan Liu

Traditionally, research about trust assumes a single type of trust between users.

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