Search Results for author: Hua-Wei Shen

Found 13 papers, 6 papers with code

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

no code implementations27 Jul 2020 Bingbing Xu, Jun-Jie Huang, Liang Hou, Hua-Wei Shen, Jinhua Gao, Xue-Qi Cheng

Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node.

Classification General Classification +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

A Non-negative Symmetric Encoder-Decoder Approach for Community Detection

1 code implementation CIKM 2019 Bing-Jie Sun, Hua-Wei Shen, Jinhua Gao, Wentao Ouyang, Xue-Qi Cheng

Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.

Community Detection Graph Clustering +2

Signed Graph Attention Networks

1 code implementation26 Jun 2019 Junjie Huang, Hua-Wei Shen, Liang Hou, Xue-Qi Cheng

We evaluate the proposed SiGAT method by applying it to the signed link prediction task.

Graph Attention Link Prediction +2

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

Modeling and Predicting Popularity Dynamics via Deep Learning Attention Mechanism

no code implementations6 Nov 2018 Sha Yuan, Yu Zhang, Jie Tang, Hua-Wei Shen, Xingxing Wei

Here we propose a deep learning attention mechanism to model the process through which individual items gain their popularity.

Marked Temporal Dynamics Modeling based on Recurrent Neural Network

no code implementations14 Jan 2017 Yongqing Wang, Shenghua Liu, Hua-Wei Shen, Xue-Qi Cheng

Indeed, in marked temporal dynamics, the time and the mark of the next event are highly dependent on each other, requiring a method that could simultaneously predict both of them.

IMRank: Influence Maximization via Finding Self-Consistent Ranking

no code implementations17 Feb 2014 Suqi Cheng, Hua-Wei Shen, Junming Huang, Wei Chen, Xue-Qi Cheng

Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2)Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.

Social and Information Networks Data Structures and Algorithms F.2.2; D.2.8

StaticGreedy: solving the scalability-accuracy dilemma in influence maximization

no code implementations19 Dec 2012 Suqi Cheng, Hua-Wei Shen, Junming Huang, Guoqing Zhang, Xue-Qi Cheng

We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization.

Social and Information Networks Data Structures and Algorithms Physics and Society F.2.2; D.2.8

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