no code implementations • 27 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.
1 code implementation • 27 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.
2 code implementations • 19 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.
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.
1 code implementation • 26 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.
Ranked #1 on Link Sign Prediction on Slashdot
1 code implementation • 21 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.
no code implementations • 20 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.
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.
Ranked #51 on Node Classification on Pubmed
no code implementations • 6 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.
no code implementations • 14 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.
no code implementations • Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) 2016 • Tong Man, Hua-Wei Shen, Shenghua Liu, Xiaolong Jin, and Xueqi Cheng
Predicting anchor links across social networks has important implications to an array of applications, including cross-network information diffusion and cross-domain recommendation.
no code implementations • 17 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
no code implementations • 19 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