Abstract Recently, distributed processing of large dynamic graphs has become very popular, especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed parallel graph processing systems have been proposed, such as Pregel, PowerGraph, GraphLab, and Trinity. However, these systems deal only with static graphs and do not consider the issue of processing evolving and dynamic graphs. In this paper, we are considering the issues of scale and dynamism in the case of graph processing systems. We present bladyg , a graph processing framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of bladyg on top of akka framework. We experimentally evaluate the performance of the proposed framework by applying it to problems such as distributed k-core decomposition and partitioning of large dynamic graphs. The experimental results show that the performance and scalability of bladyg are satisfying for large-scale dynamic graphs.
* Processing of large and dynamic graphs Chronos @cite_20 is an execution and storage engine designed for running in-memory iterative graph computation on evolving graphs. Locality is an important aspect of Chronos, where the in-memory layout of temporal graphs and the scheduling of the iterative computation on temporal and evolving graphs are carefully designed. The design of Chronos further explores the interesting interplay among locality, parallelism, and incremental computation in supporting common mining tasks on temporal graphs. We notice that traditional graph processing frameworks arrange computation around each vertex edge in a graph; while temporal graph engines, in addition, calculate the result across multiple snapshots. Chronos makes a decision to batch operations associated with each vertex (or each edge) across multiple snapshots, instead of batching operations for vertices edges within a certain snapshot @cite_20 .
@cite_20
2097805736
Temporal graphs capture changes in graphs over time and are becoming a subject that attracts increasing interest from the research communities, for example, to understand temporal characteristics of social interactions on a time-evolving social graph. Chronos is a storage and execution engine designed and optimized specifically for running in-memory iterative graph computation on temporal graphs. Locality is at the center of the Chronos design, where the in-memory layout of temporal graphs and the scheduling of the iterative computation on temporal graphs are carefully designed, so that common "bulk" operations on temporal graphs are scheduled to maximize the benefit of in-memory data locality. The design of Chronos further explores the interesting interplay among locality, parallelism, and incremental computation in supporting common mining tasks on temporal graphs. The result is a high-performance temporal-graph system that offers up to an order of magnitude speedup for temporal iterative graph mining compared to a straightforward application of existing graph engines on a series of snapshots.