Search Results for author: Yiqi Wang

Found 16 papers, 8 papers with code

Test-Time Training for Graph Neural Networks

no code implementations17 Oct 2022 Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, Xing Xie

To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.

Graph Classification Self-Supervised Learning

A Comprehensive Survey on Trustworthy Recommender Systems

no code implementations21 Sep 2022 Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.

Fairness Recommendation Systems

Are Graph Neural Networks Really Helpful for Knowledge Graph Completion?

1 code implementation21 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.

Graph Neural Networks for Multimodal Single-Cell Data Integration

1 code implementation3 Mar 2022 Hongzhi Wen, Jiayuan Ding, Wei Jin, Yiqi Wang, 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.

Data Integration

HousE: Knowledge Graph Embedding with Householder Parameterization

1 code implementation16 Feb 2022 Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang

The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.

Knowledge Graph Embedding Relation Mapping

Gophormer: Ego-Graph Transformer for Node Classification

no code implementations25 Oct 2021 Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, Yanfang Ye

Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.

Classification Data Augmentation +3

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.

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

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

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

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

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

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