activity
20182025
most citedHop-Hop Relation-aware Graph Neural Networks

4 citations · 10 across the 6 of their papers we have counts for

collaborators

6 papers

cs.AI2025

Towards deployment-centric multimodal AI beyond vision and language

Xianyuan Liu, Jiayang Zhang, Shuo Zhou +45

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as h…

q-fin.ST2023★ 1 cited

Higher-order Graph Attention Network for Stock Selection with Joint Analysis

Yang Qiao, Yiping Xia, Xiang Li +2

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to thei…

cs.IR2022★ 4 cited

Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation

Li Zhang, Yan Ge, Jun Ma +2

Cross-domain recommendation (CDR) can help customers find more satisfying items in different domains. Existing CDR models mainly use common users or mapping functions as bridges be…

cs.SI2020★ 1 cited

Unifying Homophily and Heterophily Network Transformation via Motifs

Yan Ge, Jun Ma, Li Zhang +1

Higher-order proximity (HOP) is fundamental for most network embedding methods due to its significant effects on the quality of node embedding and performance on downstream network…

cs.LG2020★ 4 cited

Hop-Hop Relation-aware Graph Neural Networks

Li Zhang, Yan Ge, Haiping Lu

Graph Neural Networks (GNNs) are widely used in graph representation learning. However, most GNN methods are designed for either homogeneous or heterogeneous graphs. In this paper,…

cs.LG2018

Mixed-Order Spectral Clustering for Networks

Yan Ge, Haiping Lu, Pan Peng

Clustering is fundamental for gaining insights from complex networks, and spectral clustering (SC) is a popular approach. Conventional SC focuses on second-order structures (e.g.,…