JGraphT -- A Java library for graph data structures and algorithms

Mathematical software and graph-theoretical algorithmic packages to efficiently model, analyze and query graphs are crucial in an era where large-scale spatial, societal and economic network data are abundantly available. One such package is JGraphT, a programming library which contains very efficient and generic graph data-structures along with a large collection of state-of-the-art algorithms. The library is written in Java with stability, interoperability and performance in mind. A distinctive feature of this library is the ability to model vertices and edges as arbitrary objects, thereby permitting natural representations of many common networks including transportation, social and biological networks. Besides classic graph algorithms such as shortest-paths and spanning-tree algorithms, the library contains numerous advanced algorithms: graph and subgraph isomorphism; matching and flow problems; approximation algorithms for NP-hard problems such as independent set and TSP; and several more exotic algorithms such as Berge graph detection. Due to its versatility and generic design, JGraphT is currently used in large-scale commercial, non-commercial and academic research projects. In this work we describe in detail the design and underlying structure of the library, and discuss its most important features and algorithms. A computational study is conducted to evaluate the performance of JGraphT versus a number of similar libraries. Experiments on a large number of graphs over a variety of popular algorithms show that JGraphT is highly competitive with other established libraries such as NetworkX or the BGL.Comment: Major Revisio

Parallel Algorithms for Counting Problems on Graphs Using Graphics Processing Units

The availability of Graphics Processing Units (GPUs) with multicore architecture have enabled parallel computations using extensive multi-threading. Recent advancements in computer hardware have led to the usage of graphics processors for solving general purpose problems. Using GPUs for computation is a highly efficient and low-cost alternative as compared to currently available multicore Central Processing Units (CPUs). Also, in the past decade there has been tremendous growth in the World Wide Web and Online Social Networks. Social networking sites such as Facebook, Twitter and LinkedIn, with millions of users are a huge source of data. These data sets can be used for research in the fields of anthropology, social psychology, economics among others. Our research focuses on converting real-world problems into graph theoretic problems and using GPUs to solve them. The graph problems that we focus on in our research involve counting the number of subgraphs that satisfy a given property. For example, given a graph G=(V,E) and an integer k<=|V|, we provide algorithms to count the number of: a) connected subgraphs of size k; b) cliques of size k; and c) independent sets of size k, and other similar problems. Also, properties that are affected by the dynamic nature of the graphs i.e., addition or removal of edges or nodes, for example change in the number of triangles and connected components in the graph, are also studied. Sequential access to global memory and contention at the size-limited shared memory have been main impediments to fully exploiting potential performance in GPUs. Therefore, we propose novel memory storage and retrieval methods, based on using search techniques on graphs and converting it into trees, that enable parallel graph computations to overcome the above issues. We also analyze and utilize primitives such as memory access coalescing and avoiding partition camping that offset the increase in access latency of using a slower but larger global memory. In addition, we introduce graph compression techniques that further reduce memory requirements and overheads. Our experimental results for the GPU implementation show a significant speedup over the CPU counterpart for the problems described above

A survey of parameterized algorithms and the complexity of edge modification

The survey is a comprehensive overview of the developing area of parameterized algorithms for graph modification problems. It describes state of the art in kernelization, subexponential algorithms, and parameterized complexity of graph modification. The main focus is on edge modification problems, where the task is to change some adjacencies in a graph to satisfy some required properties. To facilitate further research, we list many open problems in the area.publishedVersio