295 citations · 354 across the 24 of their papers we have counts for
24 papers
Instruction-based Hypergraph Pretraining
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to a downstream task, such as link prediction or…
Semi-Supervised Clustering via Structural Entropy with Different Constraints
Guangjie Zeng, Hao Peng, Angsheng Li +4
Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior information in the form of constraints to improve the quality of clustering outcomes. Desp…
Multi-view Graph Convolution for Participant Recommendation
Xiaolong Liu, Liangwei Yang, Chen Wang +3
Social networks have become essential for people's lives. The proliferation of web services further expands social networks at an unprecedented scale, leading to immeasurable comme…
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
Xiaolong Liu, Liangwei Yang, Zhiwei Liu +4
Personalized recommender systems aim to predict users' preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form g…
Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection
Jiaqian Ren, Hao Peng, Lei Jiang +4
Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies…
Unified Pretraining for Recommendation via Task Hypergraphs
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Although pretraining has garnered significant attention and popularity in recent years, its application in graph-based recommender systems is relatively limited. It is challenging…
Knowledge Graph Context-Enhanced Diversified Recommendation
Xiaolong Liu, Liangwei Yang, Zhiwei Liu +4
The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of acc…
Collaborative Semantic Alignment in Recommendation Systems
Chen Wang, Liangwei Yang, Zhiwei Liu +4
Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced ri…
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
Guangjie Zeng, Hao Peng, Angsheng Li +4
Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The complex features of pixels in the lesion region impede the lesion segmentation accuracy, and exist…
Graph-based Alignment and Uniformity for Recommendation
Liangwei Yang, Zhiwei Liu, Chen Wang +4
Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hy…
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identifi…
Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation
Yuwei Cao, Liangwei Yang, Chen Wang +4
Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The ID-based approaches completely fail to work. C…
Sequential Recommendation with Controllable Diversification: Representation Degeneration and Diversity
Ziwei Fan, Zhiwei Liu, Hao Peng +1
Sequential recommendation (SR) models the dynamic user preferences and generates the next-item prediction as the affinity between the sequence and items, in a joint latent space wi…
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Ziwei Fan, Zhiwei Liu, Hao Peng +1
Self-supervised sequential recommendation significantly improves recommendation performance by maximizing mutual information with well-designed data augmentations. However, the mut…
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-base…
MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System
Liangwei Yang, Shen Wang, Jibing Gong +4
Knowledge graph (KG) enhanced recommendation has demonstrated improved performance in the recommendation system (RecSys) and attracted considerable research interest. Recently the…
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer
Ziwei Fan, Zhiwei Liu, Chen Wang +3
Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the 'was interacted before' item-item…
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Jiaqian Ren, Lei Jiang, Hao Peng +6
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link p…
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
Jiaqian Ren, Lei Jiang, Hao Peng +3
The rising popularity of online social network services has attracted lots of research on mining social media data, especially on mining social events. Social event detection, due…
Relational Graph Neural Network Design via Progressive Neural Architecture Search
Ailing Zeng, Minhao Liu, Zhiwei Liu +3
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful in…
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
Zhiwei Liu, Yingtong Dou, Philip S. Yu +2
The graph-based model can help to detect suspicious fraud online. Owing to the development of Graph Neural Networks~(GNNs), prior research work has proposed many GNN-based fraud de…
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
Zhiwei Liu, Mengting Wan, Stephen Guo +2
Within-basket recommendation reduces the exploration time of users, where the user's intention of the basket matters. The intent of a shopping basket can be retrieved from both use…
A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
Aditya Mantha, Yokila Arora, Shubham Gupta +4
With growing consumer adoption of online grocery shopping through platforms such as Amazon Fresh, Instacart, and Walmart Grocery, there is a pressing business need to provide relev…
Unsupervised Meta-path Reduction on Heterogeneous Information Networks
Xiaokai Wei, Zhiwei Liu, Lichao Sun +1
Heterogeneous Information Network (HIN) has attracted much attention due to its wide applicability in a variety of data mining tasks, especially for tasks with multi-typed objects.…