74 citations · 135 across the 8 of their papers we have counts for
8 papers
Retrieval-Augmented Generation with Hierarchical Knowledge
Haoyu Huang, Yongfeng Huang, Junjie Yang +5
Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
A Representation Learning Framework for Property Graphs
Yifan Hou, Hongzhi Chen, Changji Li +2
Representation learning on graphs, also called graph embedding, has demonstrated its significant impact on a series of machine learning applications such as classification, predict…
Measuring and Improving the Use of Graph Information in Graph Neural Networks
Yifan Hou, Jian Zhang, James Cheng +4
Graph neural networks (GNNs) have been widely used for representation learning on graph data. However, there is limited understanding on how much performance GNNs actually gain fro…
G-Tran: Making Distributed Graph Transactions Fast
Hongzhi Chen, Changji Li, Chenguang Zheng +4
Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the rel…
Norm-Ranging LSH for Maximum Inner Product Search
Xiao Yan, Jinfeng Li, Xinyan Dai +2
Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee. We found that the perform…
Scalable De Novo Genome Assembly Using Pregel
Da Yan, Hongzhi Chen, James Cheng +2
De novo genome assembly is the process of stitching short DNA sequences to generate longer DNA sequences, without using any reference sequence for alignment. It enables high-throug…
G-thinker: Big Graph Mining Made Easier and Faster
Da Yan, Hongzhi Chen, James Cheng +3
This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and c…