Search Results for author: Qi Cao

Found 18 papers, 10 papers with code

Adversarial Camouflage for Node Injection Attack on Graphs

no code implementations3 Aug 2022 Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Liang Hou, Xueqi Cheng

Despite the initial success of node injection attacks, we find that the injected nodes by existing methods are easy to be distinguished from the original normal nodes by defense methods and limiting their attack performance in practice.

Augmentation-Aware Self-Supervision for Data-Efficient GAN Training

no code implementations31 May 2022 Liang Hou, Qi Cao, HuaWei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng

The invariance may degrade the representation learning ability of the discriminator, thereby affecting the generative modeling performance of the generator.

Data Augmentation Representation Learning

Single Node Injection Attack against Graph Neural Networks

1 code implementation30 Aug 2021 Shuchang Tao, Qi Cao, HuaWei Shen, JunJie Huang, Yunfan Wu, Xueqi Cheng

In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.

Signed Bipartite Graph Neural Networks

1 code implementation22 Aug 2021 JunJie Huang, HuaWei Shen, Qi Cao, Shuchang Tao, Xueqi Cheng

Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.

Link Sign Prediction Network Embedding

Conditional GANs with Auxiliary Discriminative Classifier

2 code implementations21 Jul 2021 Liang Hou, Qi Cao, HuaWei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng

Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.

Conditional Image Generation

INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering

1 code implementation12 Jul 2021 Yunfan Wu, Qi Cao, HuaWei Shen, Shuchang Tao, Xueqi Cheng

INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table.

Collaborative Filtering Recommendation Systems

Self-Supervised GANs with Label Augmentation

1 code implementation NeurIPS 2021 Liang Hou, HuaWei Shen, Qi Cao, Xueqi Cheng

Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment.

Data Augmentation Image Generation +2

A Non-sequential Approach to Deep User Interest Model for CTR Prediction

no code implementations5 Apr 2021 Keke Zhao, Xing Zhao, Qi Cao, Linjian Mo

The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets.

Click-Through Rate Prediction

Towards Powerful Graph Neural Networks: Diversity Matters

no code implementations1 Jan 2021 Xu Bingbing, HuaWei Shen, Qi Cao, YuanHao Liu, Keting Cen, Xueqi Cheng

For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.

Graph Representation Learning Node Classification

Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning

no code implementations30 Dec 2020 Mingqi Yuan, Qi Cao, Man-on Pun, Yi Chen

In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness.

Distributed Optimization Fairness +2

Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning

1 code implementation27 Jul 2020 Bingbing Xu, Hua-Wei Shen, Qi Cao, Keting Cen, Xue-Qi Cheng

Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data.

Adversarial Immunization for Certifiable Robustness on Graphs

2 code implementations19 Jul 2020 Shuchang Tao, Hua-Wei Shen, Qi Cao, Liang Hou, Xue-Qi Cheng

Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.

Adversarial Attack Bilevel Optimization +2

IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality

no code implementations12 Jul 2020 Di Xu, Zhen Li, Yanning Zhang, Qi Cao

This paper presents an illumination estimation method for virtual objects in real environment by learning.

Popularity Prediction on Social Platforms with Coupled Graph Neural Networks

1 code implementation21 Jun 2019 Qi Cao, Hua-Wei Shen, Jinhua Gao, Bingzheng Wei, Xue-Qi Cheng

In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction.

ANAE: Learning Node Context Representation for Attributed Network Embedding

no code implementations20 Jun 2019 Keting Cen, Hua-Wei Shen, Jinhua Gao, Qi Cao, Bingbing Xu, Xue-Qi Cheng

In this paper, we address attributed network embedding from a novel perspective, i. e., learning node context representation for each node via modeling its attributed local subgraph.

General Classification Link Prediction +2

Graph Wavelet Neural Network

1 code implementation ICLR 2019 Bingbing Xu, Hua-Wei Shen, Qi Cao, Yunqi Qiu, Xue-Qi Cheng

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.

General Classification

DeepHawkes: Bridging the gap between prediction and understanding of information cascades

1 code implementation CIKM 2017 Qi Cao, HuaWei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng

In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.

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