4 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 1 cited
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
cs.SI2014★ 4 cited
Active Community Detection in Massive Graphs
Heng Wang, Da Zheng, Randal Burns +1
A canonical problem in graph mining is the detection of dense communities. This problem is exacerbated for a graph with a large order and size -- the number of vertices and edges -…
cs.DC2014★ 2 cited
FlashGraph: Processing Billion-Node Graphs on an Array of Commodity SSDs
Da Zheng, Disa Mhembere, Randal Burns +3
Graph analysis performs many random reads and writes, thus, these workloads are typically performed in memory. Traditionally, analyzing large graphs requires a cluster of machines…