4 citations · 18 across the 22 of their papers we have counts for
9 papers · 1 filter
PyG-SSL: A Graph Self-Supervised Learning Toolkit
Lecheng Zheng, Baoyu Jing, Zihao Li +9
Graph Self-Supervised Learning (SSL) has emerged as a pivotal area of research in recent years. By engaging in pretext tasks to learn the intricate topological structures and prope…
APEX: Adaptive and Extreme Summarization for Personalized Knowledge Graphs
Zihao Li, Dongqi Fu, Mengting Ai +1
Knowledge graphs (KGs), which store an extensive number of relational facts, serve various applications. Recently, personalized knowledge graphs (PKGs) have emerged as a solution t…
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
Zihao Li, Lecheng Zheng, Bowen Jin +5
While great success has been achieved in building vision models with Contrastive Language-Image Pre-training (CLIP) over internet-scale image-text pairs, building transferable Grap…
Local Clustering on Complex Graphs and Complex Hypergraphs
Zihao Li, Dongqi Fu, Hengyu Liu +1
Local/seeded clustering aims to find a compact cluster near the given starting instances. While most existing studies on graph clustering assume a discrete graph setting (i.e., unw…
PageRank Bandits for Link Prediction
Yikun Ban, Jiaru Zou, Zihao Li +5
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been dire…
Hypergraphs as Weighted Directed Self-Looped Graphs: Spectral Properties, Clustering, Cheeger Inequality
Zihao Li, Dongqi Fu, Hengyu Liu +1
Hypergraphs naturally arise when studying group relations and have been widely used in the field of machine learning. To the best of our knowledge, the recently proposed edge-depen…
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs
Dongqi Fu, Liri Fang, Zihao Li +3
Graphs, as a relational data structure, have been widely used for various application scenarios, like molecule design and recommender systems. Recently, large language models (LLMs…
SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
Zihao Li, Yuyi Ao, Jingrui He
Knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expre…
Heterogeneous Contrastive Learning for Foundation Models and Beyond
Lecheng Zheng, Baoyu Jing, Zihao Li +2
In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing f…