Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Modellierung und Analyse komplexer Systeme


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 Intel⃝(R)-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. Index Terms:
Social network analysis, streaming data, graph analysis, parallel processing