As graph data is prevalent for an increasing number of Internet applications, continuously monitoring structural patterns in dynamic graphs in order to generate real-time alerts and trigger prompt actions becomes critical for many applications. In this paper, we present a new system GraphS to efficiently detect constrained cycles in a dynamic graph, which is changing constantly, and return the satisfying cycles in real-time. A hot point based index is built and efficiently maintained for each query so as to greatly speed-up query time and achieve high system throughput. The GraphS system is developed at Alibaba to actively monitor various online fraudulent activities based on cycle detection. For a dynamic graph with hundreds of millions of edges and vertices, the system is capable to cope with a peak rate of tens of thousands of edge updates per second and find all the cycles with predefined constraints with a 99.9% latency of 20 milliseconds.
Network virtualization provides a promising way to overcome Internet ossification. A major challenge is virtual network mapping, i.e., how to embed multiple virtual network requests with resource constraints into a substrate network, such that physical resources are utilized in an efficient and effective manner. Since this problem is known to be NP-complete, a variety of heuristic algorithms have been proposed. In this paper, we reexamine this problem and propose a virtual network mapping framework, ORS T A, which is based on Opportunistic Resource Sharing and Topology-Aware node ranking. Opportunistic resource sharing is taken into consideration at the entire network level for the first time and we develop an online approximation algorithm, FFA, for solving the corresponding time slot assignment problem. To measure the topology importance of a substrate node, a node ranking method, MCRank, based on Markov chain is presented. We also devise a simple and practical method to estimate the residual resource of a substrate node/link. Extensive simulation experiments demonstrate that the proposed framework enables the substrate network to achieve efficient physical resource utilization and to accept many more virtual network requests over time.
Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view of distributed subgraph matching mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art algorithms. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representative algorithms. We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
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