Distributed Near-Maximum Independent Set Maintenance over Large-scale Dynamic Graphs.

Computing the maximum independent set (MIS) in a graph is a fundamental NP-hard problem, which is widely adopted in many real-world applications. Extensive works have been done on computing an approximate MIS. While the highly dynamic property of real-world graphs calls for efficient MIS maintenance solutions, existing works for dynamic MIS computation in the literature mainly focus on the single-machine scenario. The assumption that a single machine can access the whole graph makes them difficult to be straightforwardly applied for large-scale graphs in distributed environment. Motivated by this, in this paper, we study the problem of maintaining approximate MIS over large-scale dynamic graphs in distributed environments. We propose a new vertex centric algorithm OIMIS. Compared with existing solutions, OIMIS avoids the strong order dependency in distributed computation, which makes it easy to handle dynamic graph updates. OIMIS computes and maintains MIS with high effectiveness and efficiency. In terms of high effectiveness, OIMIS maintains consistent MIS results with the state-of-the-art distributed algorithm to compute MIS in static graphs. In terms of high efficiency, each vertex in OIMIS only updates MIS status according to its neighbor attributes. Novel optimization techniques are also designed to reduce communication and computation cost. We conduct extensive experiments to prove the effectiveness and efficiency of our distributed algorithms.