Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileAcademic lineage
Possible advisorsa guess from early papers, not confirmed
- Huan LiuSuggested from co-authorship
Is this you? Claim this profile to confirm or dismiss it.
Works374 from public data
- Fake News Detection on Social Media3,450
This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia.
- Graph Neural Networks for Social Recommendation2,435
This paper provides a principled approach to jointly capture interactions and opinions in the user-item graph and proposes the framework GraphRec, which coherently models two graphs and heterogeneous strengths for social recommendations.
- Feature Selection2,424
This survey revisits feature selection research from a data perspective and reviews representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data, and categorizes them into four main groups: similarity- based, information-theoretical-based, sparse-learning-based and statistical-based.
- Graph Structure Learning for Robust Graph Neural Networks975
A general framework Pro-GNN is proposed, which can jointly learn a structural graph and a robust graph neural network model from the perturbed graph guided by these properties, and achieves significantly better performance compared with the state-of-the-art defense methods, even when the graph is heavily perturbed.
- A Survey on Dialogue Systems788
This article generally divides existing dialogue systems into task-oriented and nontask- oriented models, then detail how deep learning techniques help them with representative algorithms and finally discusses some appealing research directions that can bring the dialogue system research into a new frontier.
- Adversarial Attacks and Defenses in Images, Graphs and Text: A Review772
A systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures against adversarial examples, for three most popular data types, including images, graphs and text is reviewed.
- 757
- 739
- Traffic Flow Prediction via Spatial Temporal Graph Neural Network681
A novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns and provides a sequential component to model the traffic flow dynamics which can exploit both local and global temporal dependencies.
- Recommender Systems in the Era of Large Language Models (LLMs)604
This survey comprehensively review LLM-empowered recommender systems from various perspectives including pre-training, fine-tuning, and prompting paradigms, and comprehensively discusses the promising future directions in this emerging field.
- 581
- 504
- Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs479
This paper aims to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigates two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors.
- 462
- Deep reinforcement learning for page-wise recommendations454
A principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page is proposed and a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users is proposed.
- 424
- Large Language Models for Education: A survey and outlook423
A comprehensive technological overview for educators, researchers, and policy makers to harness the power of LLMs, revolutionize educational practices, and foster a more effective personalized learning environment is provided.
- Attributed Network Embedding for Learning in a Dynamic Environment396
DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner, and performs extensive experiments to corroborate the effectiveness and efficiency of the proposed framework.
- Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning394
This paper model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedback.
- mTrust390
Experimental results on real-world data from Epinions and Ciao show that the work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory.
- Graph Convolutional Networks with EigenPooling371
A pooling operator based on graph Fourier transform is introduced, which can utilize the node features and local structures during the pooling process and is combined with traditional GCN convolutional layers to form a graph neural network framework for graph classification.
- A Survey of Signed Network Mining in Social Media368
A review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers of signed network mining.
- 345
- Node Similarity Preserving Graph Convolutional Networks336
This work proposes a feature similarity preserving aggregation which adaptively integrates graph structure and node features and employs self-supervised learning to explicitly capture the complex feature similarity and dissimilarity relations between nodes.
- Trustworthy AI: A Computational Perspective310
A comprehensive appraisal of trustworthy AI from a computational perspective to help readers understand the latest technologies for achieving trustworthy AI and focuses on six of the most crucial dimensions.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.