QuateXelero : an accelerated exact network motif detection algorithm

Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network

DwarvesGraph: A High-Performance Graph Mining System with Pattern Decomposition

This paper presents DwarvesGraph, the first graph mining system that decomposes the target pattern into several subpatterns, and then computes the count of each. The results of the target pattern can be calculated using the subpattern counts with very low additional cost. Despite decomposition-based algorithms have been studied for years, we propose several novel techniques to address key system challenges: 1) a partial-embedding-centric programming model with efficient supports for pattern existence query and advanced graph mining applications such as FSM; 2) an accurate and efficient cost model based on approximate graph mining; 3) an efficient search method to jointly determine the decomposition of all concrete patterns of an application, considering the computation cost and cross-pattern computation reuse; and 4) the partial symmetry breaking technique to eliminate redundant enumeration for each subpattern while preserving equivalence of computation. Our experiments show that DwarvesGraph is significantly faster than all existing state-of-the-art systems and provides a novel and viable path to scale to large patterns

Peregrine: A Pattern-Aware Graph Mining System

Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. General purpose graph mining systems provide a generic runtime to explore subgraph structures of interest with the help of user-defined functions that guide the overall exploration process. However, the state-of-the-art graph mining systems remain largely oblivious to the shape (or pattern) of the subgraphs that they mine. This causes them to: (a) explore unnecessary subgraphs; (b) perform expensive computations on the explored subgraphs; and, (c) hold intermediate partial subgraphs in memory; all of which affect their overall performance. Furthermore, their programming models are often tied to their underlying exploration strategies, which makes it difficult for domain users to express complex mining tasks. In this paper, we develop Peregrine, a pattern-aware graph mining system that directly explores the subgraphs of interest while avoiding exploration of unnecessary subgraphs, and simultaneously bypassing expensive computations throughout the mining process. We design a pattern-based programming model that treats "graph patterns" as first class constructs and enables Peregrine to extract the semantics of patterns, which it uses to guide its exploration. Our evaluation shows that Peregrine outperforms state-of-the-art distributed and single machine graph mining systems, and scales to complex mining tasks on larger graphs, while retaining simplicity and expressivity with its "pattern-first" programming approach.Comment: This is the full version of the paper appearing in the European Conference on Computer Systems (EuroSys), 202