22 citations · 65 across the 14 of their papers we have counts for
4 papers · 2 filters
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Nian Liu, Xiaoxin He, Thomas Laurent +3
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing…
Can we Soft Prompt LLMs for Graph Learning Tasks?
Zheyuan Liu, Xiaoxin He, Yijun Tian +1
Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks. In recent years, Larg…
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
Yufei He, Yuan Sui, Xiaoxin He +1
Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence, exhibiting remarkable abilities to generalize across a wide array of tasks and applications be…
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Xiaoxin He, Yijun Tian, Yifei Sun +5
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user…