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The vertex-centric programming model is an established computational paradigm recently
incorporated into distributed processing frameworks to address challenges in large-scale …

Large-scale graph-structured computation is central to tasks ranging from targeted
advertising to natural language processing and has led to the development of several graph …

In pursuit of graph processing performance, the systems community has largely abandoned
general-purpose distributed dataflow frameworks in favor of specialized graph processing …

There has been significant recent interest in parallel frameworks for processing graphs due
to their applicability in studying social networks, the Web graph, networks in biology, and …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

From social networks to targeted advertising, big graphs capture the structure in data and
are central to recent advances in machine learning and data mining. Unfortunately, directly …

Traditionally distributed graph processing systems have largely focused on scalability
through the optimizations of inter-node communication and load balance. However, they …

Natural graphs with skewed distributions raise unique challenges to distributed graph
computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” …

Graphs are one of the key data structures for many real-world computing applications and
the importance of graph analytics is ever-growing. While existing software graph processing …