k-way edge based partitioning algorithms for processing large streaming graphs, such as social networks and web crawls, assign each arriving edge to one of the k partitions. This can result in vertices being replicated on multiple partitions. Typically, such partitioning algorithms aim to balance the edge counts across partitions while minimizing the vertex replication. However, such objectives ignore the community structure inherently embedded in the graph, which is an important quality metric for clustering and graph mining applications that subsequently operate on the partitions. To address this gap, we propose a novel optimization goal to maximize the number of local triangles in the partitions as an additional objective. Triangle count is an effective metric to measure the conservation of community structure. Further, we propose TriParts a family of heuristics for online partitioning over an edge stream. They use three complementary state data structures: Bloom Filters, Triangle Map and High degree Map. Each state adds tangible value to meet our objectives. We validate TriParts on six diverse real world graphs with up to 1.6B edges and varying triangle densities. Our best heuristic outperforms the state-of-the-art DBH and HDRF streaming graph partitioners on the triangle-count metric by up to 4–8.3x while maintaining competitive vertex replication factor and edge-balancing. We achieve an ingest rate of 500k edges/sec on a 16 node cluster. We also offer detailed results on the configuration parameters, scalability and overheads of TriParts, and its practical benefits for distributed graph analytics.

Temporal graphs change with time and have a lifespan associated with each vertex and edge. These graphs are suitable to process time-respecting algorithms where the traversed edges must have monotonic timestamps. Interval-centric Computing Model (ICM) is a distributed programming abstraction to design such temporal algorithms. There has been little work on supporting time-respecting algorithms at large scales for streaming graphs, which are updated continuously at high rates (Millions/s), such as in financial and social networks. In this article, we extend the windowed-variant of ICM for incremental computing over streaming graph updates. We formalize the properties of temporal graph algorithms and prove that our model of incremental computing over streaming updates is equivalent to batch execution of ICM. We design TARIS, a novel distributed graph platform that implements these incremental computing features. We use efficient data structures to reduce memory access and enhance locality during graph updates. We also propose scheduling strategies to interleave updates with computing, and streaming strategies to adapt the execution window for incremental computing to the variable input rates. Our detailed and rigorous evaluation of temporal algorithms on large-scale graphs with up to$2\,\text{B}$edges show that TARIS out-performs contemporary baselines, Tink and Gradoop, by 3–4 orders of magnitude, and handles a high input rate of$ 83k$–$ 587\,\text{M}$Mutations/s with latencies in the order of seconds–minutes.