26 citations · 30 across the 18 of their papers we have counts for
15 papers · 1 filter
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…
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…
Tabular Foundation Models are Strong Graph Anomaly Detectors
Yunhui Liu, Tieke He, Yongchao Liu +3
Graph anomaly detection (GAD), which aims to identify abnormal nodes that deviate from the majority, has become increasingly important in high-stakes Web domains. However, existing…
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…
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…
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…
Transferable and Forecastable User Targeting Foundation Model
Bin Dou, Baokun Wang, Yun Zhu +11
User targeting, the process of selecting targeted users from a pool of candidates for non-expert marketers, has garnered substantial attention with the advancements in digital mark…
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 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…