SociaLite: Datalog Extensions for Efficient Social Network Analysis

Abstract

With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently. We evaluated SociaLite by running eight graph algorithms (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) on two real-life social graphs, Live-Journal and Last.fm. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50% for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, giving up only 16% for the largest benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to make social network queries easily and efficiently.