157 citations · 201 across the 5 of their papers we have counts for
5 papers
The Future is Big Graphs! A Community View on Graph Processing Systems
Sherif Sakr, Angela Bonifati, Hannes Voigt +38
Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users a…
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…
Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs
Da Yan, James Cheng, M. Tamer Özsu +5
Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly "think like a vertex" programming…
Efficient Processing of Very Large Graphs in a Small Cluster
Da Yan, Yuzhen Huang, James Cheng +1
Inspired by the success of Google's Pregel, many systems have been developed recently for iterative computation over big graphs. These systems provide a user-friendly vertex-centri…
Effective Techniques for Message Reduction and Load Balancing in Distributed Graph Computation
Da Yan, James Cheng, Yi Lu +1
Massive graphs, such as online social networks and communication networks, have become common today. To efficiently analyze such large graphs, many distributed graph computing syst…