Search Results for author: Suhang Wang

Found 39 papers, 18 papers with code

Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

no code implementations1 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 Message Passing Graph Neural Network on Heterophilic Graphs

no code implementations15 Oct 2021 Enyan Dai, Zhimeng Guo, Suhang Wang

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

Node Classification

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.

Text 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.

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

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.

Graph Convolutional Network Time Series

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

no code implementations29 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.

Fairness

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.

General Classification Graph Learning +1

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

no code implementations23 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.

General Classification Time Series +1

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

1 code implementation3 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.

Fairness General Classification +1

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.

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.

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

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

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.)

Link Prediction MULTI-VIEW LEARNING +2

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

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

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-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

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

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

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