26 citations · 30 across the 15 of their papers we have counts for
15 papers
Learning Hierarchical Knowledge in Text-Rich Networks with Taxonomy-Informed Representation Learning
Yunhui Liu, Yongchao Liu, Yinfeng Chen +3
Hierarchical knowledge structures are ubiquitous across real-world domains and play a vital role in organizing information from coarse to fine semantic levels. While such structure…
Query as Anchor: Scenario-Adaptive User Representation via Large Language Model
Jiahao Yuan, Yike Xu, Jinyong Wen +9
Industrial-scale user representation learning requires balancing robust universality with acute task-sensitivity. However, existing paradigms primarily yield static, task-agnostic…
Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
Yunhui Liu, Pengyu Qiu, Yu Xing +6
Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patterns in graph-structured data. De…
Text2GraphQuery-Bench: A Text to Graph Query Benchmark
Songlin Lyu, Lujie Ban, Zihang Wu +12
Graph models are fundamental to data analysis in domains rich with complex relationships. Unlike SQL, which benefits from a rel- atively unified standard and widespread familiarity…
Instruction-aware User Embedding via Synergistic Language and Representation Modeling
Ziyi Gao, Yike Xu, Jiahao Yuan +9
User representation modeling has become increasingly crucial for personalized applications, yet existing approaches struggle with generalizability across domains and sensitivity to…
M^2VAE: Multi-Modal Multi-View Variational Autoencoder for Cold-start Item Recommendation
Chuan He, Yongchao Liu, Qiang Li +3
Cold-start item recommendation is a significant challenge in recommendation systems, particularly when new items are introduced without any historical interaction data. While exist…
GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs
Yue Jin, Yongchao Liu, Chuntao Hong
Graph-based computations are crucial in a wide range of applications, where graphs can scale to trillions of edges. To enable efficient training on such large graphs, mini-batch su…
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
Xiabao Wu, Yongchao Liu, Wei Qin +1
Graph neural networks (GNNs) have delivered remarkable results in various fields. However, the rapid increase in the scale of graph data has introduced significant performance bott…
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation
Chuan He, Yongchao Liu, Qiang Li +5
Sequential recommendation (SR) aims to predict the next purchasing item according to users' dynamic preference learned from their historical user-item interactions. To improve the…
GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphs
Sheng Tian, Xintan Zeng, Yifei Hu +7
Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological…
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering
Boci Peng, Yongchao Liu, Xiaohe Bo +4
Commonsense question answering is a crucial task that requires machines to employ reasoning according to commonsense. Previous studies predominantly employ an extracting-and-modeli…
GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
Yun Zhu, Haizhou Shi, Xiaotang Wang +5
Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to the prevalence of free-text node features in real-world applications and the advancement…
Graph Retrieval-Augmented Generation: A Survey
Boci Peng, Yun Zhu, Yongchao Liu +5
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By ref…
Graph Triple Attention Network: A Decoupled Perspective
Xiaotang Wang, Yun Zhu, Haizhou Shi +2
Graph Transformers (GTs) have recently achieved significant success in the graph domain by effectively capturing both long-range dependencies and graph inductive biases. However, t…
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks
Xiaotang Wang, Yun Zhu, Haizhou Shi +2
In the graph domain, deep graph networks based on Message Passing Neural Networks (MPNNs) or Graph Transformers often cause over-smoothing of node features, limiting their expressi…