Search Results for author: Huan Liu

Found 89 papers, 26 papers with code

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.

14

A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning

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

Recently, increasing attention has been devoted to the graph few-shot learning problem, where the target novel classes only contain a few labeled nodes.

Contrastive Learning Data Augmentation +2

Text Transformations in Contrastive Self-Supervised Learning: A Review

no code implementations22 Mar 2022 Amrita Bhattacharjee, Mansooreh Karami, Huan Liu

However, in the domain of Natural Language, the augmentation methods used in creating similar pairs with regard to contrastive learning assumptions are challenging.

Contrastive Learning Representation Learning +1

Few-Shot Learning on Graphs: A Survey

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: A Survey

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

In order to compensate for this inability, physics-informed graph learning (PIGL) is emerging.

Graph Learning

Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning

no code implementations17 Feb 2022 Kaize Ding, Yancheng Wang, Yingzhen Yang, Huan Liu

Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizable, transferable, and robust node representations in a self-supervised fashion.

Contrastive Learning Representation Learning

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

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

"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.

Causal Inference

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 Incremental Learning +2

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

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 +1

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 Zero-Shot Object Detection

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.

Weakly-supervised Graph Meta-learning for 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.

Combinatorial Optimization Graph Learning +2

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.

Fairness

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

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

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 +2

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.

Fairness

"Let's Eat Grandma": When Punctuation Matters in Sentence Representation for Sentiment Analysis

no code implementations10 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

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.

Misinformation

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

Long-Term Effect Estimation with Surrogate Representation

no code implementations19 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 +1

Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation

no code implementations9 Mar 2020 Raha Moraffah, Mansooreh Karami, Ruocheng Guo, Adrienne Raglin, Huan Liu

In this work, models that aim to answer causal questions are referred to as causal interpretable models.

Decision Making

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.

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 Multi-Task Learning

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

1 code implementation 2019 SIAM International Conference on Data Mining (SDM) 2019 Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu

In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data.

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

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

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

1 code implementation21 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

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.

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

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).

online learning

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.

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

5 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

no code implementations9 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

2 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

5 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.

Link Prediction Network Embedding +1

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

Feature Selection: A Data Perspective

1 code implementation29 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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