49 citations · 102 across the 14 of their papers we have counts for
14 papers
MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval
Xixi Wu, Yanchao Tan, Nan Hou +2
Document Understanding is a foundational AI capability with broad applications, and Document Question Answering (DocQA) is a key evaluation task. Traditional methods convert the do…
When Do LLMs Help With Node Classification? A Comprehensive Analysis
Xixi Wu, Yifei Shen, Fangzhou Ge +4
Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-ba…
ProCom: A Few-shot Targeted Community Detection Algorithm
Xixi Wu, Kaiyu Xiong, Yun Xiong +4
Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying f…
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Xi Chen, Yun Xiong, Siwei Zhang +7
Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from bo…
Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling
Siwei Zhang, Xi Chen, Yun Xiong +5
Temporal Graph Networks (TGNs) have demonstrated their remarkable performance in modeling temporal interaction graphs. These works can generate temporal node representations by enc…
Can Graph Learning Improve Planning in LLM-based Agents?
Xixi Wu, Yifei Shen, Caihua Shan +8
Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests i…
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Yucheng Jin, Yun Xiong, Juncheng Fang +5
Node classification on graphs is of great importance in many applications. Due to the limited labeling capability and evolution in real-world open scenarios, novel classes can emer…
Prompt Learning on Temporal Interaction Graphs
Xi Chen, Siwei Zhang, Yun Xiong +6
Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems. To facilitate representation learning on TIGs, researchers have proposed a series of TIG mod…
DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning
Yingying Wang, Yun Xiong, Xixi Wu +2
Drug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction hav…
Graph Prompt Learning: A Comprehensive Survey and Beyond
Xiangguo Sun, Jiawen Zhang, Xixi Wu +3
Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This pape…
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation
Siwei Zhang, Yun Xiong, Yao Zhang +3
Continuous-time dynamic graph modeling is a crucial task for many real-world applications, such as financial risk management and fraud detection. Though existing dynamic graph mode…
Dual Intents Graph Modeling for User-centric Group Discovery
Xixi Wu, Yun Xiong, Yao Zhang +2
Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recomme…
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Xixi Wu, Yun Xiong, Yao Zhang +4
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation tas…
CLARE: A Semi-supervised Community Detection Algorithm
Xixi Wu, Yun Xiong, Yao Zhang +5
Community detection refers to the task of discovering closely related subgraphs to understand the networks. However, traditional community detection algorithms fail to pinpoint a p…