26 citations · 30 across the 18 of their papers we have counts for
9 papers · 1 filter
Scaling Graph Chain-of-Thought Reasoning: A Multi-Agent Framework with Efficient LLM Serving
Chengying Huan, Ziheng Meng, Yongchao Liu +11
Graph Chain-of-Thought (Graph-CoT) enables large language models (LLMs) to perform step-by-step reasoning over graph-structured knowledge, but existing pipelines suffer from low ac…
Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses
Runlin Lei, Lu Yi, Mingguo He +4
While Graph Neural Networks (GNNs) and Large Language Models (LLMs) are powerful approaches for learning on Text-Attributed Graphs (TAGs), a comprehensive understanding of their ro…
Rethinking and Benchmarking Large Language Models for Graph Reasoning
Yuwei Hu, Xinyi Huang, Zhewei Wei +2
Large Language Models (LLMs) for Graph Reasoning have been extensively studied over the past two years, involving enabling LLMs to understand graph structures and reason on graphs…
FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation
Chuan He, Yang Chen, Bin Dou +10
User representation learning serves as a fundamental pillar for personalized services on large-scale web platforms. Despite its importance, conventional continuous embedding method…
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…
GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning
Jie Peng, Jiarui Ji, Runlin Lei +3
Dynamic Text-Attributed Graphs (DyTAGs), which intricately integrate structural, temporal, and textual attributes, are crucial for modeling complex real-world systems. However, mos…
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…
Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs
Runlin Lei, Jiarui Ji, Haipeng Ding +4
With the rise of large language models (LLMs), there has been growing interest in Graph Foundation Models (GFMs) for graph-based tasks. By leveraging LLMs as predictors, GFMs have…