Many applications need to handle very large graphs, including theweb graph, social network graph, knowledge graphs, biology graphs, etc. We are facing challenges at all levels from system infrastructures to programmingmodels formanaging and analyzing large graphs. Unlike other types of big data, graphs are highly interconnected, which enables graph to represent complex data structures in a variety of applications, but at the same time, also makes graph query processing and graph analytics extremely difficult, especially when the graph is big. Much effort has been devoted to managing andminingmassive graphs. Thesework include graph systems, graphquery languages, graph access methods, basic operators such as graph reachability and shortest distance queries, and advanced analytics on graphs. There is also a big push from the application side. Linked data, social networks, customer relationship management, biological and chemical applications are all pushing for breakthroughs in this area. This special issue presents high quality research ideas related to the graph data management and mining area. The first paper of this issue: The G* Graph Database: Efficient Managing Large Distributed Dynamic Graphs by Alan G. Labouseur, Jeremy Birnbaum, PaulW. Olsen Jr., Sean R. Spillane, Jayadevan Vijayan, Jeong-Hyon Hwang, and Wook-Shin Han presents a graph database system G* to process complex queries over large graphs in parallel with the features of sharing computation across graphs.