Romāns Kasperovičs

Finding subgraph isomorphisms is an important problem in many applications which deal with data modeled as graphs. While this problem is NP-hard, in recent years, many algorithms have been proposed to solve it in a reasonable time for real datasets using different join orders, pruning rules, and auxiliary neighborhood information. However, since they have not been empirically compared one another in most research work, it is not clear whether the later work outperforms the earlier work. Another problem is that reported comparisons were often done using the original authors' binaries which were written in different programming environments. In this paper, we address these serious problems by re-implementing five state-of-the-art subgraph isomorphism algorithms in a common code base and by comparing them using many real-world datasets and their query loads. Through our in-depth analysis of experimental results, we report surprising empirical findings.

Graphs provide a powerful way to model complex structures such as chemical compounds, proteins, images, and program dependence. The previous practice for experiments in graph indexing techniques is that the author of a newly proposed technique does not implement existing indexes on his own code base, but instead uses the original authors' binary executables and reports only the wall clock time. However, we observed that this practice may result in several problems [6]. In order to address these problems, we have implemented all representative graph indexing techniques on a common framework called iGraph [6]. In this demonstration we showcase iGraph and its visual tools using several real datasets and their workloads. For selected queries of the workloads, we show several unique features including visual performance analysis.

Real-world graph applications are typically domain-specific and model complex business processes in the property graph data model. To implement a domain-specific graph algorithm in the context of such a graph application, simply providing a set of built-in graph algorithms is usually not sufficient nor does it allow algorithm customization to the user's needs. To cope with these issues, graph database vendors provide---in addition to their declarative graph query languages---procedural interfaces to write user-defined graph algorithms.

Regular Path Queries (RPQs) are at the core of many recent declarative graph pattern matching languages. They leverage the compactness and expressiveness of regular expressions for matching recursive path structures. Unfortunately, most prior works on RPQs only consider breadth-first search as traversal strategy, neglecting other possible graph traversals like depth-first search or a combination of both. Within this paper, we conduct an analysis of graph traversals for RPQs by introducing a generalized graph traversal frame-work subsuming breadth-first search and depth-first search as extreme cases and thus opening up a new design space for graph traversals algorithms. We outline the underlying principles as well as provide comprehensive experimental evaluation using implementations which yield beneficial results regarding evaluation time and peak memory consumption.