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In this work, we present EAGr, a system for supporting large numbers ofcontinuous neighborhood-based ("ego-centric") aggregate queries over large,highly dynamic, and rapidly evolving graphs. Examples of such queries includecomputation of personalized, tailored trends in social networks, anomaly/eventdetection in financial transaction networks, local search and alerts inspatio-temporal networks, to name a few. Key challenges in supporting suchcontinuous queries include high update rates typically seen in thesesituations, large numbers of queries that need to be executed simultaneously,and stringent low latency requirements. We propose a flexible, general, andextensible in-memory framework for executing different types of ego-centricaggregate queries over large dynamic graphs with low latencies. Our frameworkis built around the notion of an aggregation overlay graph, a pre-compiled datastructure that encodes the computations to be performed when an update/query isreceived. The overlay graph enables sharing of partial aggregates acrossmultiple ego-centric queries (corresponding to the nodes in the graph), andalso allows partial pre-computation of the aggregates to minimize the querylatencies. We present several highly scalable techniques for constructing anoverlay graph given an aggregation function, and also design incrementalalgorithms for handling structural changes to the underlying graph. We alsopresent an optimal, polynomial-time algorithm for making the pre-computationdecisions given an overlay graph, and evaluate an approach to incrementallyadapt those decisions as the workload changes. Although our approach isnaturally parallelizable, we focus on a single-machine deployment and show thatour techniques can easily handle graphs of size up to 320 million nodes andedges, and achieve update/query throughputs of over 500K/s using a single,powerful machine.

EAGr: Supporting Continuous Ego-centric Aggregate Queries over Large Dynamic Graphs