Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs
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 advancements in …
the prevalence of free-text node features in real-world applications and the advancements in …
Graph Reasoning with LLMs (GReaL)
Graphs are a powerful tool for representing and analyzing complex relationships in real-
world applications. Large Language Models (LLMs) have demonstrated impressive …
world applications. Large Language Models (LLMs) have demonstrated impressive …
TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features
Tabular machine learning is an important field for industry and science. In this field, table
rows are usually treated as independent data samples, but additional information about …
rows are usually treated as independent data samples, but additional information about …
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs
Graph Neural Networks (GNNs) have emerged as a powerful tool to capture intricate
network patterns, achieving success across different domains. However, existing GNNs …
network patterns, achieving success across different domains. However, existing GNNs …
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model
Graph foundation models (GFMs) have recently gained significant attention. However, the
unique data processing and evaluation setups employed by different studies hinder a …
unique data processing and evaluation setups employed by different studies hinder a …
Can LLMs Convert Graphs to Text-Attributed Graphs?
Graphs are ubiquitous data structures found in numerous real-world applications, such as
drug discovery, recommender systems, and social network analysis. Graph neural networks …
drug discovery, recommender systems, and social network analysis. Graph neural networks …