Given a directed graph G, to check whether a node v is reachable from another node u through a path is often required. In a database system, such an operation is called a recursion computation or reachability checking and not efficiently supported. The reason for this is that the space to store the whole transitive closure of G is prohibitively high. In this paper, we address this issue and propose an 0(n2 + bnradic(b)) time algorithm to decompose a directed acyclic graph (DAG) into a minimized set of disjoint chains to facilitate reachability checking, where n is the number of the nodes and b is the DAG's width, defined to be the size of a largest node subset U of the DAG such that for every pair of nodes u, v isin U, there does not exist a path from u to v or from v to u. Using this algorithm, we are able to label a graph in 0(be) time and store all the labels in O(bn) space with O(logb) reachability checking time, where e is the number of the edges of the DAG. The method can also be extended to handle cyclic directed graphs. Experiments have been performed, showing that our method is promising.

An Efficient Algorithm for Answering Graph Reachability Queries

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Let G(V, E) be a digraph (directed graph) with n nodes and e edges. Digraph G* = (V, E*) is the reflexive, transitive closure if (v, u) ∈ E* iff there is a path from v to u in G. Efficient storage of G* is important for supporting reachability queries which are not only common on graph databases, but also serve as fundamental operations used in many graph algorithms. A lot of strategies have been suggested based on the graph labeling, by which each node is assigned with certain labels such that the reachability of any two nodes through a path can be determined by their labels. Among them are interval labelling, chain decomposition, and 2-hop labeling. However, due to the very large size of many real world graphs, the computational cost and size of labels using existing methods would prove too expensive to be practical. In this paper, we propose a new approach to decompose a graph into a series of spanning trees which may share common edges, to transform a reachability query over a graph into a set of queries over trees. We demonstrate both analytically and empirically the efficiency and effectiveness of our method.

/pdf/decomposing-dags-into-spanning-trees-a-new-way-to-compress-1awobjm5xk.pdf

Decomposing DAGs into spanning trees: A new way to compress transitive closures

Any existing transitive closure in a database can be found using a method based on standard 4th Generation Structured Query Language (SQL) without non-standard extensions to perform recursion analysis. There are no requirements to stay within a preset number of levels in the database to prevent breakdown as in previous known methods. The method represents each database as a graph and builds path and edge tables which are updated as data, that is verticies and edges, are added or deleted. Since this method uses instructions that are entirely within standard 4GL SQL, it can be used over multiple platforms as long as the platforms are 4GL SQL compliant.

Process for providing transitive closure using fourth generation structure query language (SQL)

Graph reachability is fundamental to a wide range of applications, including CAD/CAM, CASE, office systems, software management, as well as geographical navigation and internet routing. Many applications involve huge graphs and requires fast answering of reachability queries. Several reachability labeling methods have been proposed for this purpose. They assign labels to the nodes, such that the reachability between any two nodes can be determined using their labels only. In this paper, we propose a new data structure, called a general spanning tree of a directed acyclic graph (DAG) to minimize label space. Different from a traditional spanning tree, an edge in a general spanning tree T of a DAG G may corresponds to a path in G. That is, for each edge u → v in T, we have a path from u to v in G. An algorithm is discussed to find such a tree with the least number of leaf nodes in O(bn √b) time, where n is the number of the nodes of G, and b is the number of the leaf nodes of T. It can be proven that b equals G's width, defined to be the size of a largest node subset U of G such that for every pair of nodes u, v ∈ U, there does not exist a path from u to v or from v to u. Based on T, we are able to reduce the label space to O(bn) with O(logb) reachability query time. Our method can also be extended for graphs containing cycles.

/pdf/general-spanning-trees-and-reachability-query-evaluation-w74nh66yis.pdf

General spanning trees and reachability query evaluation

Composite objects often involve recursive relationships, so-called bills-of-materials, which are cumbersome to handle in relational database systems. The relationships constitute a directed graph, where the successors of a node represent its components, recursively. Instead of the whole transitive closure (all ancestor-descendant pairs), the task is to retrieve the descendants of any given node. A simple relational solution is suggested, which packs information of the ancestor path of each node into a fixed-length code, called the signature. The code is nonunique, and its purpose is to define a relatively small superset of the descendants, as well as to establish a basis for clustering. It supports effective retrieval of the descendants, in terms of both disk accesses and DBMS calls. The method performs best for tree-structured graphs, where the processing time typically decreases by a factor of more than 10, compared to a simple loop of joins. Also, general directed graphs, both acyclic and cyclic, can be processed more effectively. The method is implemented on top of a relational system, but advantages can be gained on other platforms, too.

Path signatures: a way to speed up recursion in relational databases

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Path signatures: a way to speed up recursion in relational databases