Analyzing static snapshots of massive, graph-structured data
cannot keep pace with the growth of social networks, financial
transactions, and other valuable data sources. We introduce
a framework, STING (Spatio-Temporal Interaction Networks
and Graphs), and evaluate its performance on multicore, multisocket
Intel ® -based platforms. STING achieves rates of
around 100 000 edge updates per second on large, dynamic
graphs with a single, general data structure. We achieve speedups
of up to 1000× over parallel static computation, improve
monitoring a dynamic graph's connected components, and
show an exact algorithm for maintaining local clustering coefficients
performs better on Intel-based platforms than our
earlier approximate algorithm.
