Search Results for author: Jinhua Gao

Found 7 papers, 3 papers with code

Few-Shot Stance Detection via Target-Aware Prompt Distillation

no code implementations27 Jun 2022 Yan Jiang, Jinhua Gao, HuaWei Shen, Xueqi Cheng

The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets.

Few-Shot Learning Stance Detection

Modelling Universal Order Book Dynamics in Bitcoin Market

no code implementations15 Jan 2021 Fabin Shi, Nathan Aden, Shengda Huang, Neil Johnson, Xiaoqian Sun, Jinhua Gao, Li Xu, HuaWei Shen, Xueqi Cheng, Chaoming Song

Understanding the emergence of universal features such as the stylized facts in markets is a long-standing challenge that has drawn much attention from economists and physicists.

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

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

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

Cascade Dynamics Modeling with Attention-based Recurrent Neural Network

1 code implementation1 May 2017 Yongqing Wang, HuaWei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng

However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade— one sharing behavior could be triggered by its non-immediate predecessor in the memory chain.

Marketing

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