Search Results for author: Jiliang Tang

Found 91 papers, 35 papers with code

Are Graph Neural Networks Really Helpful for Knowledge Graph Completion?

no code implementations21 May 2022 Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin

This suggests a conflation of scoring function design, loss function design, and aggregation in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable aggregation designs for KGC tasks tomorrow.

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

no code implementations18 Apr 2022 Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, Suhang Wang

Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.

Drug Discovery Fairness

Graph Enhanced BERT for Query Understanding

no code implementations3 Apr 2022 Juanhui Li, Yao Ma, Wei Zeng, Suqi Cheng, Jiliang Tang, Shuaiqiang Wang, Dawei Yin

In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.

Graph Neural Networks for Multimodal Single-Cell Data Integration

1 code implementation3 Mar 2022 Hongzhi Wen, Jiayuan Ding, Wei Jin, Yuying Xie, Jiliang Tang

Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics.

Graph Neural Networks with Adaptive Residual

1 code implementation NeurIPS 2021 Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang

Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.

Graph Representation Learning

Graph Condensation for Graph Neural Networks

1 code implementation ICLR 2022 Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah

Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns.

Towards Feature Overcorrelation in Deeper Graph Neural Networks

no code implementations29 Sep 2021 Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

In this paper, we observe a new issue in deeper GNNs, i. e., feature overcorrelation, and perform a thorough study to deepen our understanding on this issue.

Graph Representation Learning

What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020

no code implementations20 Sep 2021 Jamell Dacon, Jiliang Tang

Consequently, our findings highlight that social activism done by Black Lives Matter activists does not diverge from the social issues and topics involving police-brutality related and racially-motivated killings of Black individuals due to the shape of its topical graph that topics and conversations encircling the largest component directly relate to the topic of Black Lives Matter.

Graph Trend Filtering Networks for Recommendations

1 code implementation12 Aug 2021 Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li

The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.

Collaborative Filtering Graph Representation Learning +1

Decentralized Composite Optimization with Compression

no code implementations10 Aug 2021 Yao Li, Xiaorui Liu, Jiliang Tang, Ming Yan, Kun Yuan

Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottleneck in practice.

Jointly Attacking Graph Neural Network and its Explanations

no code implementations7 Aug 2021 Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, JianPing Wang, Charu Aggarwal

Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.

Imbalanced Adversarial Training with Reweighting

no code implementations28 Jul 2021 Wentao Wang, Han Xu, Xiaorui Liu, Yaxin Li, Bhavani Thuraisingham, Jiliang Tang

Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks.

Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models

1 code implementation15 Jul 2021 Qiongqiong Liu, Tianqiao Liu, Jiafu Zhao, Qiang Fang, Wenbiao Ding, Zhongqin Wu, Feng Xia, Jiliang Tang, Zitao Liu

Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.

Sentence Completion

Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models

1 code implementation15 Jul 2021 Yang Hao, Hang Li, Wenbiao Ding, Zhongqin Wu, Jiliang Tang, Rose Luckin, Zitao Liu

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits.

Multi-Task Learning

Trustworthy AI: A Computational Perspective

no code implementations12 Jul 2021 Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, Jiliang Tang

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.


Elastic Graph Neural Networks

1 code implementation5 Jul 2021 Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang

While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing.

AutoLoss: Automated Loss Function Search in Recommendations

no code implementations12 Jun 2021 Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang

Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.

Recommendation Systems

Is Homophily a Necessity for Graph Neural Networks?

no code implementations ICLR 2022 Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang

We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions.

Node Classification

Automated Self-Supervised Learning for Graphs

1 code implementation ICLR 2022 Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang

Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.

Node Classification Node Clustering +1

Towards the Memorization Effect of Neural Networks in Adversarial Training

no code implementations9 Jun 2021 Han Xu, Xiaorui Liu, Wentao Wang, Wenbiao Ding, Zhongqin Wu, Zitao Liu, Anil Jain, Jiliang Tang

In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.

Adversarial Robustness

Graph Feature Gating Networks

no code implementations10 May 2021 Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang

Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.


The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification

no code implementations Findings (ACL) 2021 Haochen Liu, Wei Jin, Hamid Karimi, Zitao Liu, Jiliang Tang

The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.

Classification Fairness +2

Node Similarity Preserving Graph Convolutional Networks

1 code implementation19 Nov 2020 Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang

Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.

Graph Representation Learning Self-Supervised Learning

Personalized Multimodal Feedback Generation in Education

no code implementations COLING 2020 Haochen Liu, Zitao Liu, Zhongqin Wu, Jiliang Tang

The automatic evaluation for school assignments is an important application of AI in the education field.

Text Generation

To be Robust or to be Fair: Towards Fairness in Adversarial Training

1 code implementation13 Oct 2020 Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, Jiliang Tang

However, we find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data.


A Unified View on Graph Neural Networks as Graph Signal Denoising

1 code implementation5 Oct 2020 Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah

In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption.


Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning

1 code implementation EMNLP 2020 Haochen Liu, Wentao Wang, Yiqi Wang, Hui Liu, Zitao Liu, Jiliang Tang

Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.

Dialogue Generation

Representation Learning from Limited Educational Data with Crowdsourced Labels

1 code implementation23 Sep 2020 Wentao Wang, Guowei Xu, Wenbiao Ding, Gale Yan Huang, Guoliang Li, Jiliang Tang, Zitao Liu

Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines.

Face Recognition Machine Translation +1

Yet Meta Learning Can Adapt Fast, It Can Also Break Easily

no code implementations2 Sep 2020 Han Xu, Ya-Xin Li, Xiaorui Liu, Hui Liu, Jiliang Tang

Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem.

Few-Shot Image Classification Meta-Learning

Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection

no code implementations31 Aug 2020 Zhiwei Wang, Zhengzhang Chen, Jingchao Ni, Hui Liu, Haifeng Chen, Jiliang Tang

To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences.

Anomaly Detection

Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks

no code implementations28 Jun 2020 Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang

Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.

Self-Supervised Learning

Memory-efficient Embedding for Recommendations

no code implementations26 Jun 2020 Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long

Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.

AutoML Recommendation Systems

XGNN: Towards Model-Level Explanations of Graph Neural Networks

no code implementations3 Jun 2020 Hao Yuan, Jiliang Tang, Xia Hu, Shuiwang Ji

Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.

Graph Generation

Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models

no code implementations27 May 2020 Haochen Liu, Zhiwei Wang, Tyler Derr, Jiliang Tang

Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society.

Customized Graph Neural Networks

no code implementations22 May 2020 Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang

Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.

Classification General Classification +1

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

no code implementations17 May 2020 Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jian-Ping Wang, Jiliang Tang, Qing Li

In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.

Data Poisoning Recommendation Systems

Siamese Neural Networks for Class Activity Detection

no code implementations15 May 2020 Hang Li, Zhiwei Wang, Jiliang Tang, Wenbiao Ding, Zitao Liu

Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms.

Action Detection Activity Detection

DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses

3 code implementations13 May 2020 Ya-Xin Li, Wei Jin, Han Xu, Jiliang Tang

DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field.

Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies

3 code implementations2 Mar 2020 Wei Jin, Ya-Xin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang

As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability.

Adversarial Attack

Jointly Learning to Recommend and Advertise

no code implementations28 Feb 2020 Xiangyu Zhao, Xudong Zheng, Xiwang Yang, Xiaobing Liu, Jiliang Tang

Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations

no code implementations26 Feb 2020 Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, Jiliang Tang

Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.

AutoML Recommendation Systems

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

Graduate Employment Prediction with Bias

no code implementations27 Dec 2019 Teng Guo, Feng Xia, Shihao Zhen, Xiaomei Bai, Dongyu Zhang, Zitao Liu, Jiliang Tang

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.

Characterizing the Decision Boundary of Deep Neural Networks

1 code implementation24 Dec 2019 Hamid Karimi, Tyler Derr, Jiliang Tang

In this regard, one crucial aspect of deep neural network classifiers that can help us deepen our knowledge about their decision-making behavior is to investigate their decision boundaries.

Decision Making

Learning Multi-level Dependencies for Robust Word Recognition

2 code implementations22 Nov 2019 Zhiwei Wang, Hui Liu, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu

Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken with the presence of noises.

Does Gender Matter? Towards Fairness in Dialogue Systems

1 code implementation COLING 2020 Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu, Jiliang Tang

In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.


A Double Residual Compression Algorithm for Efficient Distributed Learning

no code implementations16 Oct 2019 Xiaorui Liu, Yao Li, Jiliang Tang, Ming Yan

Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms.

Automatic Short Answer Grading via Multiway Attention Networks

no code implementations23 Sep 2019 Tiaoqiao Liu, Wenbiao Ding, Zhiwei Wang, Jiliang Tang, Gale Yan Huang, Zitao Liu

Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads.

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

4 code implementations17 Sep 2019 Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain

In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.

Adversarial Attack

Say What I Want: Towards the Dark Side of Neural Dialogue Models

no code implementations13 Sep 2019 Haochen Liu, Tyler Derr, Zitao Liu, Jiliang Tang

Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations.


DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems

no code implementations9 Sep 2019 Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Jiliang Tang, Hui Liu

However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).

Recommendation Systems reinforcement-learning

Deep Knowledge Tracing with Side Information

no code implementations1 Sep 2019 Zhiwei Wang, Xiaoqin Feng, Jiliang Tang, Gale Yan Huang, Zitao Liu

Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems.

Knowledge Tracing

Recommender Systems with Heterogeneous Side Information

1 code implementation18 Jul 2019 Tianqiao Liu, Zhiwei Wang, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu

In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items.

Recommendation Systems

Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study

1 code implementation18 Jul 2019 Guowei Xu, Wenbiao Ding, Jiliang Tang, Songfan Yang, Gale Yan Huang, Zitao Liu

In practice, the crowdsourced labels are usually inconsistent among crowd workers given their diverse expertise and the number of crowdsourced labels is very limited.

Representation Learning

Deep Social Collaborative Filtering

1 code implementation16 Jul 2019 Wenqi Fan, Yao Ma, Dawei Yin, Jian-Ping Wang, Jiliang Tang, Qing Li

Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.

Collaborative Filtering Recommendation Systems

Toward Simulating Environments in Reinforcement Learning Based Recommendations

no code implementations27 Jun 2019 Xiangyu Zhao, Long Xia, Lixin Zou, Dawei Yin, Jiliang Tang

Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.

Recommendation Systems reinforcement-learning

Attacking Graph Convolutional Networks via Rewiring

no code implementations10 Jun 2019 Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, Jiliang Tang

Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.

General Classification Graph Classification +2

Deep Adversarial Social Recommendation

1 code implementation30 May 2019 Wenqi Fan, Tyler Derr, Yao Ma, JianPing Wang, Jiliang Tang, Qing Li

Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.

Recommendation Systems Representation Learning

Graph Convolutional Networks with EigenPooling

1 code implementation30 Apr 2019 Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang

To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded.

Classification General Classification +4

Deep Adversarial Network Alignment

no code implementations27 Feb 2019 Tyler Derr, Hamid Karimi, Xiaorui Liu, Jiejun Xu, Jiliang Tang

Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure.

Graph Embedding Network Embedding

Learning Hierarchical Discourse-level Structure for Fake News Detection

2 code implementations NAACL 2019 Hamid Karimi, Jiliang Tang

Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured.

Fake News Detection

Graph Neural Networks for Social Recommendation

6 code implementations19 Feb 2019 Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin

These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.

Ranked #3 on Recommendation Systems on Epinions (using extra training data)

Recommendation Systems

Whole-Chain Recommendations

no code implementations11 Feb 2019 Xiangyu Zhao, Long Xia, Linxin Zou, Hui Liu, Dawei Yin, Jiliang Tang

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.

Multi-agent Reinforcement Learning Recommendation Systems

Deep reinforcement learning for search, recommendation, and online advertising: a survey

no code implementations18 Dec 2018 Xiangyu Zhao, Long Xia, Jiliang Tang, Dawei Yin

Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.


Streaming Graph Neural Networks

2 code implementations24 Oct 2018 Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin

Current graph neural network models cannot utilize the dynamic information in dynamic graphs.

Community Detection General Classification +3

Signed Graph Convolutional Network

3 code implementations ICDM 2018 Tyler Derr, Yao Ma, Jiliang Tang

However, since previous GCN models have primarily focused on unsigned networks (or graphs consisting of only positive links), it is unclear how they could be applied to signed networks due to the challenges presented by negative links.

Social and Information Networks Physics and Society

Linked Recurrent Neural Networks

no code implementations19 Aug 2018 Zhiwei Wang, Yao Ma, Dawei Yin, Jiliang Tang

Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation.

Document Classification Machine Translation +2

Multi-dimensional Graph Convolutional Networks

no code implementations18 Aug 2018 Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang

Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video.

Social and Information Networks

Multi-Source Multi-Class Fake News Detection

no code implementations COLING 2018 Hamid Karimi, Proteek Roy, Sari Saba-Sadiya, Jiliang Tang

Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years.

Fake News Detection

Deep Reinforcement Learning for Page-wise Recommendations

no code implementations7 May 2018 Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang

In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.

Recommendation Systems reinforcement-learning

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

1 code implementation19 Feb 2018 Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, Dawei Yin

Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.

Recommendation Systems reinforcement-learning

Deep Reinforcement Learning for List-wise Recommendations

7 code implementations30 Dec 2017 Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, Jiliang Tang

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.

Recommendation Systems reinforcement-learning

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

Streaming Recommender Systems

no code implementations21 Jul 2016 Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios.

Recommendation Systems

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/}).

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.

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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