SuMGra: Querying Multigraphs via Efficient Indexing

Abstract

Abstract. Many real world datasets can be represented by a network with a set of nodes interconnected with each other by multiple relations. Such a rich graph is called a multigraph. Unfortunately, all the existing algorithms for subgraph query matching are not able to adequately leverage multiple relationships that exist between the nodes. In this paper we propose an efficient indexing schema for querying single large multigraphs, where the indexing schema aptly captures the neighbourhood structure in the data graph. Our proposal SuMGra couples this novel indexing schema with a subgraph search algorithm to quickly traverse though the solution space to enumerate all the matchings. Extensive experiments conducted on real benchmarks prove the time efficiency as well as the scalability of SuMGra .

Visual querying of large multilayer graphs

“…Our approach has three main functionalities: i) a graph visualization component to show the whole graph database; ii) an interface to graphically specify multilayer query graphs and iii) an interactive system to browse and visualize query result patterns at different levels of detail. Furthermore, our tool not only provides the visualization functionalities but it also tightly integrates the multilayer graph engine [6] that processes the query and retrieves the result patterns from the multilayer graph database.…”

“…Since a multilayer graph involves different relationships between nodes, standard query engines such as TurboISO [5] or VF3 [2] are not adapted for this kind of task. To deal with this issue we integrate in our approach a recent multilayer query engine named SuMGra [6]. This query engine exploits specialised indexing techniques in order to speed up the backtracking algorithm that is commonly employed to deal with the subgraph isomorphism problem [5].…”

“…We use the FM3 implementation supplied by the Open Graph Drawing Framework (OGDF) 2 for graph layout placement. The graph database engine [6] is also implemented in Java and integrated in our visual tool. Demonstration scenario is carried out on a Work Station with an Intel Xeon processor at 3.00 GHz with 16 GB of RAM memory with a 64 bits Windows OS.…”

“…Query efficiently this kind of data is only the first step to support end user analysis [6]. In order to facilitate such analysis, several visual query and exploration systems for graph databases exist in literature [9,11] but, to the best of our knowledge, none of them can deal with neither multilayer networks nor the exploration of the results on the original graph database.…”

Subgraph Matching on Multiplex Networks

“…It is also an important subroutine in frequent subgraph mining [28], [67] and graph database search [74]. Despite the abundance of multiplex network data in these applications, there are relatively few subgraph matching algorithms that expressly support multiplex networks [29], [43] compared to the number of algorithms that support single-channel networks [6], [9], [10], [14], [24], [57], [61]. In this paper, we introduce a new algorithm for subgraph matching on multiplex networks and discuss some simplifications of the subgraph matching problem.…”