Abstract-Graph-processing platforms are increasingly used in a variety of domains. Although both industry and academia are developing and tuning graph-processing algorithms and platforms, the performance of graph-processing platforms has never been explored or compared in-depth. Thus, users face the daunting challenge of selecting an appropriate platform for their specific application. To alleviate this challenge, we propose an empirical method for benchmarking graph-processing platforms. We define a comprehensive process, and a selection of representative metrics, datasets, and algorithmic classes. We implement a benchmarking suite of five classes of algorithms and seven diverse graphs. Our suite reports on basic (user-lever) performance, resource utilization, scalability, and various overhead. We use our benchmarking suite to analyze and compare six platforms. We gain valuable insights for each platform and present the first comprehensive comparison of graph-processing platforms.

Several organizations, like social networks, store and routinely analyze large graphs as part of their daily operation. Such graphs are typically distributed across multiple servers, and graph partitioning is critical for efficient graph management. Existing partitioning algorithms focus on finding graph partitions with good locality, but disregard the pragmatic challenges of integrating partitioning into large-scale graph management systems deployed on the cloud, such as dealing with the scale and dynamicity of the graph and the compute environment.In this paper, we propose Spinner, a scalable and adaptive graph partitioning algorithm based on label propagation designed on top of the Pregel model. Spinner scales to massive graphs, produces partitions with locality and balance comparable to the state-of-the-art and efficiently adapts the partitioning upon changes. We describe our algorithm and its implementation in the Pregel programming model that makes it possible to partition billion-vertex graphs. We evaluate Spinner with a variety of synthetic and real graphs and show that it can compute partitions with quality comparable to the state-of-the art. In fact, by using Spinner in conjunction with the Giraph graph processing engine, we speed up different applications by a factor of 2 relative to standard hash partitioning.

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Mining large graphs is critical for several real-world systems such as social networks. Such graphs typically consist of hundreds of millions of vertices and edges and are highly dynamic, with their structure continuously evolving over time. The current data deluge is making this problem even harder: bigger graphs must be processed even faster than before. Appropriate graph partitioning is crucial to scale to large graphs and reduce processing time. However, partitioning large dynamic graphs is challenging: current techniques either do not scale well as they require global graph knowledge or do not handle changes in the graph.In this work, we introduce a general purpose graph partitioning algorithm that adapts to dynamic structural changes in massive graphs. The algorithm works by iteratively adapting the partitions using only local information. We show how the application of our algorithm to three real-world scenarios can speed up the execution of graph analyses by over 50%, while adapting the partitioning to a high volume of changes to the graph.

Of the many crowd behavior models, very few have been used in assisting crowd management practice. This lack of usage is partly due to crowd management involving a diversity of situations that require competencies in observing, sense-making, anticipating and acting. Crowd research is similarly scattered across disciplines and needs integration to advance the field towards supporting practice. To address these needs, we present INCROWD, an integrated framework detailing a high-level architecture of a decisionsupport system for crowd management and model development. It also offers a lens for categorizing crowd literature, allowing us to present a structured literature review.