Search Results for author: Suhang Wang

Found 81 papers, 37 papers with code

Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective

no code implementations17 May 2024 Zhiwei Zhang, Minhua Lin, Enyan Dai, Suhang Wang

To ensure a high attack success rate with ID triggers, we introduce novel modules designed to enhance trigger memorization by the victim model trained on poisoned graph.

Backdoor Attack Memorization +1

Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs

1 code implementation10 Apr 2024 Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Suhang Wang, Yu Meng, Jiawei Han

Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively.

Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond

1 code implementation15 Mar 2024 Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang

Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.

Explanation Generation Image Generation

Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark

1 code implementation9 Mar 2024 Xiaowei Qian, Zhimeng Guo, Jialiang Li, Haitao Mao, Bingheng Li, Suhang Wang, Yao Ma

These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.

Benchmarking Fairness +1

Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks

no code implementations24 Feb 2024 Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma

The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.

Contrastive Learning Graph Learning +1

InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration

no code implementations18 Feb 2024 Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen

Though Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains, they struggle with knowledge-intensive tasks.

Knowledge Graphs

Universal Prompt Optimizer for Safe Text-to-Image Generation

1 code implementation16 Feb 2024 Zongyu Wu, Hongcheng Gao, Yueze Wang, Xiang Zhang, Suhang Wang

To guide the optimizer to have the ability of converting toxic prompt to clean prompt while preserving semantic information, we design a novel reward function measuring toxicity and text alignment of generated images and train the optimizer through Proximal Policy Optimization.

Blocking Text-to-Image Generation

Disambiguated Node Classification with Graph Neural Networks

1 code implementation13 Feb 2024 Tianxiang Zhao, Xiang Zhang, Suhang Wang

Graph Neural Networks (GNNs) have demonstrated significant success in learning from graph-structured data across various domains.

Classification Contrastive Learning +2

PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection

no code implementations6 Feb 2024 Enyan Dai, Minhua Lin, Suhang Wang

PreGIP incorporates a task-free watermarking loss to watermark the embedding space of pretrained GNN encoder.

Active Learning for Graphs with Noisy Structures

no code implementations4 Feb 2024 Hongliang Chi, Cong Qi, Suhang Wang, Yao Ma

Yet, the excessive cost of labeling large-scale graphs led to a focus on active learning on graphs, which aims for effective data selection to maximize downstream model performance.

Active Learning Node Classification

Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels

no code implementations18 Jan 2024 Fali Wang, Tianxiang Zhao, Suhang Wang

Self-training has emerged as a widely popular framework to leverage the abundance of unlabeled data, which expands the training set by assigning pseudo-labels to selected unlabeled nodes.

Node Classification

Towards Off-Policy Reinforcement Learning for Ranking Policies with Human Feedback

no code implementations17 Jan 2024 Teng Xiao, Suhang Wang

Probabilistic learning to rank (LTR) has been the dominating approach for optimizing the ranking metric, but cannot maximize long-term rewards.

Decision Making Learning-To-Rank +1

Spectral-Based Graph Neural Networks for Complementary Item Recommendation

no code implementations4 Jan 2024 Haitong Luo, Xuying Meng, Suhang Wang, Hanyun Cao, Weiyao Zhang, Yequan Wang, Yujun Zhang

In this study, we present a novel approach called Spectral-based Complementary Graph Neural Networks (SComGNN) that utilizes the spectral properties of complementary item graphs.

Attribute Recommendation Systems

Simple and Asymmetric Graph Contrastive Learning without Augmentations

1 code implementation NeurIPS 2023 Teng Xiao, Huaisheng Zhu, Zhengyu Chen, Suhang Wang

Experimental results show that the simple GraphACL significantly outperforms state-of-the-art graph contrastive learning and self-supervised learning methods on homophilic and heterophilic graphs.

Contrastive Learning Representation Learning +1

Learning Graph Filters for Spectral GNNs via Newton Interpolation

no code implementations16 Oct 2023 Junjie Xu, Enyan Dai, Dongsheng Luo, Xiang Zhang, Suhang Wang

Spectral Graph Neural Networks (GNNs) are gaining attention because they can surpass the limitations of message-passing GNNs by learning spectral filters that capture essential frequency information in graph data through task supervision.

Language Models As Semantic Indexers

1 code implementation11 Oct 2023 Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, Xianfeng Tang

Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.

Contrastive Learning Information Retrieval +2

Certifiably Robust Graph Contrastive Learning

1 code implementation NeurIPS 2023 Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang

Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method in providing effective certifiable robustness and enhancing the robustness of any GCL model.

Contrastive Learning Graph Representation Learning

Learning How to Propagate Messages in Graph Neural Networks

1 code implementation1 Oct 2023 Teng Xiao, Zhengyu Chen, Donglin Wang, Suhang Wang

To compensate for this, in this paper, we present learning to propagate, a general learning framework that not only learns the GNN parameters for prediction but more importantly, can explicitly learn the interpretable and personalized propagate strategies for different nodes and various types of graphs.

Interpretable Imitation Learning with Dynamic Causal Relations

no code implementations30 Sep 2023 Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen

After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it.

Causal Discovery Imitation Learning

Towards Fair Graph Neural Networks via Graph Counterfactual

1 code implementation10 Jul 2023 Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, Suhang Wang

Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios.

counterfactual Fairness +2

Recent Developments in Recommender Systems: A Survey

no code implementations22 Jun 2023 Yang Li, Kangbo Liu, Ranjan Satapathy, Suhang Wang, Erik Cambria

The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems.

Fairness Recommendation Systems

STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation

no code implementations21 Jun 2023 Wanda Li, Wenhao Zheng, Xuanji Xiao, Suhang Wang

Our experimental results using both public and industrial datasets demonstrate that the proposed model significantly improves multi-task prediction performance compared to state-of-the-art methods, highlighting the importance of considering user lifecycle stages in recommendation systems.

Multi-Task Learning Recommendation Systems

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

1 code implementation13 Jun 2023 Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen

Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations.

Disentanglement Imitation Learning

Self-Explainable Graph Neural Networks for Link Prediction

no code implementations21 May 2023 Huaisheng Zhu, Dongsheng Luo, Xianfeng Tang, Junjie Xu, Hui Liu, Suhang Wang

Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes.

Link Prediction Node Classification

Counterfactual Learning on Graphs: A Survey

1 code implementation3 Apr 2023 Zhimeng Guo, Teng Xiao, Zongyu Wu, Charu Aggarwal, Hui Liu, Suhang Wang

To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning.

counterfactual Fairness +2

Unnoticeable Backdoor Attacks on Graph Neural Networks

1 code implementation11 Feb 2023 Enyan Dai, Minhua Lin, Xiang Zhang, Suhang Wang

In particular, backdoor attack poisons the graph by attaching triggers and the target class label to a set of nodes in the training graph.

Backdoor Attack Graph Classification +1

Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment

no code implementations7 Jan 2023 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions.

Inductive Bias

TopoImb: Toward Topology-level Imbalance in Learning from Graphs

no code implementations16 Dec 2022 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

To address this problem, we propose a new framework {\method} and design (1 a topology extractor, which automatically identifies the topology group for each instance with explicit memory cells, (2 a training modulator, which modulates the learning process of the target GNN model to prevent the case of topology-group-wise under-representation.

HP-GMN: Graph Memory Networks for Heterophilous Graphs

1 code implementation15 Oct 2022 Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang

Graph neural networks (GNNs) have achieved great success in various graph problems.

Towards Prototype-Based Self-Explainable Graph Neural Network

no code implementations5 Oct 2022 Enyan Dai, Suhang Wang

Therefore, we study a novel problem of learning prototype-based self-explainable GNNs that can simultaneously give accurate predictions and prototype-based explanations on predictions.

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

no code implementations3 Aug 2022 Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, Suhang Wang

Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs.

Link Prediction Representation Learning

Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks

no code implementations10 Jun 2022 Tianxiang Zhao, Xiang Zhang, Suhang Wang

In many real-world scenarios, node classes are imbalanced, with some majority classes making up most parts of the graph.

Node Classification

Decoupled Self-supervised Learning for Non-Homophilous Graphs

no code implementations7 Jun 2022 Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, Suhang Wang

This paper studies the problem of conducting self-supervised learning for node representation learning on graphs.

Representation Learning Self-Supervised Learning +1

Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders

no code implementations7 Jun 2022 Teng Xiao, Zhengyu Chen, Suhang Wang

In this paper, we propose a theoretical understanding of why existing unbiased learning objectives work for unbiased recommendation.

Generalization Bounds Knowledge Distillation +3

Towards Faithful and Consistent Explanations for Graph Neural Networks

no code implementations27 May 2022 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input.

Inductive Bias Network Interpretation

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

Exploring Edge Disentanglement for Node Classification

no code implementations23 Feb 2022 Tianxiang Zhao, Xiang Zhang, Suhang Wang

Concretely, these self-supervision tasks are enforced on a designed edge disentanglement module to be trained jointly with the downstream node classification task to encourage automatic edge disentanglement.

Classification Disentanglement +2

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

1 code implementation1 Jan 2022 Enyan Dai, Wei Jin, Hui Liu, Suhang Wang

To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which can down-weight or eliminate noisy edges and facilitate message passing of GNNs to alleviate the issue of limited labeled nodes.

Label-Wise Graph Convolutional Network for Heterophilic Graphs

1 code implementation15 Oct 2021 Enyan Dai, Shijie Zhou, Zhimeng Guo, Suhang Wang

Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications.

Node Classification Representation Learning

Towards Self-Explainable Graph Neural Network

1 code implementation26 Aug 2021 Enyan Dai, Suhang Wang

Though many efforts are taken to improve the explainability of deep learning, they mainly focus on i. i. d data, which cannot be directly applied to explain the predictions of GNNs because GNNs utilize both node features and graph topology to make predictions.

Node Classification

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.

Graph Routing between Capsules

no code implementations22 Jun 2021 Yang Li, Wei Zhao, Erik Cambria, Suhang Wang, Steffen Eger

Therefore, in this paper, we introduce a new capsule network with graph routing to learn both relationships, where capsules in each layer are treated as the nodes of a graph.

Relation text-classification +1

NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs

1 code implementation8 Jun 2021 Enyan Dai, Charu Aggarwal, Suhang Wang

Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification.

Node Classification

Labeled Data Generation with Inexact Supervision

no code implementations8 Jun 2021 Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang

We propose a novel generative framework named as ADDES which can synthesize high-quality labeled data for target classification tasks by learning from data with inexact supervision and the relations between inexact supervision and target classes.


Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network

no code implementations27 May 2021 Yuqing Hu, Xiaoyuan Cheng, Suhang Wang, Jianli Chen, Tianxiang Zhao, Enyan Dai

After discussion, it is found that data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.

Time Series Analysis

Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features

1 code implementation29 Apr 2021 Tianxiang Zhao, Enyan Dai, Kai Shu, Suhang Wang

Though the sensitive attribute of each data sample is unknown, we observe that there are usually some non-sensitive features in the training data that are highly correlated with sensitive attributes, which can be used to alleviate the bias.

Attribute BIG-bench Machine Learning +1

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

2 code implementations16 Mar 2021 Tianxiang Zhao, Xiang Zhang, Suhang Wang

This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs.

Classification General Classification +2

Semi-Supervised Graph-to-Graph Translation

no code implementations16 Mar 2021 Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, Suhang Wang

For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain.

Graph-To-Graph Translation Translation

Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals

1 code implementation23 Nov 2020 Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, Vasant Honavar

Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions.

Explainable Models General Classification +3

Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information

2 code implementations3 Sep 2020 Enyan Dai, Suhang Wang

Though extensive studies of fair classification have been conducted on i. i. d data, methods to address the problem of discrimination on non-i. i. d data are rather limited.

Attribute Fairness +2

MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models

1 code implementation1 Sep 2020 Thai Le, Suhang Wang, Dongwon Lee

In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem.

Comment Generation Fake News 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

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

GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction

1 code implementation5 Nov 2019 Thai Le, Suhang Wang, Dongwon Lee

Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural network models when the datasets are tabular and their features are in high-dimensional vectorized formats.


Node Injection Attacks on Graphs via Reinforcement Learning

no code implementations14 Sep 2019 Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar

Real-world graph applications, such as advertisements and product recommendations make profits based on accurately classify the label of the nodes.

Node Classification reinforcement-learning +1

MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

no code implementations20 Aug 2019 Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, Vasant Honavar

Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e. g., friendship, shared interests in music, etc.)

Generative Adversarial Network Link Prediction +3

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

1 code implementation20 Aug 2019 Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang

To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph.

Node Classification Transfer 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 +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

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.

General Classification Graph Classification +3

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

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

7 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-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

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

Disentangled Variational Auto-Encoder for Semi-supervised Learning

no code implementations15 Sep 2017 Yang Li, Quan Pan, Suhang Wang, Haiyun Peng, Tao Yang, Erik Cambria

The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.


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

PPP: Joint Pointwise and Pairwise Image Label Prediction

no code implementations CVPR 2016 Yilin Wang, Suhang Wang, Jiliang Tang, Huan Liu, Baoxin Li

However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels.

Attribute General Classification +2

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

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