Declarative analysis of noisy information networks

Moustafa, Walaa Eldin; Namata, Galileo; Deshpande, Amol; Getoor, Lise

doi:10.1109/icdew.2011.5767619

Cited by 15 publications

(10 citation statements)

References 12 publications

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“…Attributed graphs are supported by several database management systems, but even basic "integrity constraints are poorly studied in graph databases" [Angles, 2012]. Somewhat related to our work are recursive graph query languages over advanced graph models, such as SNQL [Martín et al, 2011], GraphQL [He and Singh, 2008], G-Log [Paredaens et al, 1995], and GraphLog [Consens and Mendelzon, 1989]. As MARPL extends Datalog, it subsumes Datalog fragments such as GraphLog, but other algebras sport incomparable expressivity such as the computation of aggregates in SNQL.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Logic on MARS: Ontologies for Generalised Property Graphs

Marx

Krötzsch

Thost

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Graph-structured data is used to represent large information collections, called knowledge graphs, in many applications. Their exact format may vary, but they often share the concept that edges can be annotated with additional information, such as validity time or provenance information. Property Graph is a popular graph database format that also provides this feature. We give a formalisation of a generalised notion of Property Graphs, called multiattributed relational structures (MARS), and introduce a matching knowledge representation formalism, multi-attributed predicate logic (MAPL). We analyse the expressive power of MAPL and suggest a simpler, rule-based fragment of MAPL that can be used for ontological reasoning on Property Graphs.

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Section: Discussion and Outlookmentioning

confidence: 99%

Logic on MARS: Ontologies for Generalised Property Graphs

Marx

Krötzsch

Thost

2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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“…In the area of graph databases, several query languages have been proposed to query and manage graph data including GraphLog [13], GOOD [18], GraphDB [17], GOQL [35], PQL [24], and GRDB [29]. There is also much work on subgraph pattern matching with renewed interest in recent years.…”

Section: Related Workmentioning

confidence: 99%

Ego-centric Graph Pattern Census

Moustafa

Deshpande

Getoor

2012

2012 IEEE 28th International Conference on Data Engineering

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There is increasing interest in analyzing networks of all types including social, biological, sensor, computer, and transportation networks. Broadly speaking, we may be interested in global network-wide analysis (e.g., centrality analysis, community detection) where the properties of the entire network are of interest, or local ego-centric analysis where the focus is on studying the properties of nodes (egos) by analyzing their neighborhood subgraphs. In this paper we propose and study ego-centric pattern census queries, a new type of graph analysis query, where a given structural pattern is searched for in every node's neighborhood and the counts are reported or used in further analysis. This kind of analysis is useful in many domains in social network analysis including opinion leader identification, node classification, link prediction, and role identification. We propose an SQL-based declarative language to support this class of queries, and develop a series of efficient query evaluation algorithms for it. We evaluate our algorithms on a variety of synthetically generated graphs. We also show an application of our language in a real-world scenario for predicting future collaborations from DBLP data.

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“…A number of query languages have been proposed for graph databases, including GraphLog [9], GLog [27], GOQL [31], and GRDB [23]. These query languages support functionalities that are useful for graph analysis, such as subgraph matching and node traversal.…”

Section: Related Workmentioning

confidence: 99%

SociaLite: Datalog extensions for efficient social network analysis

Seo

Guo

Lam

2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

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Abstract-With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead.For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently.We evaluated SociaLite by running eight graph algorithms (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) on two real-life social graphs, LiveJournal and Last.fm. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50% for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, giving up only 16% for the largest benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to make social network queries easily and efficiently.

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Declarative analysis of noisy information networks

Cited by 15 publications

References 12 publications

Logic on MARS: Ontologies for Generalised Property Graphs

Logic on MARS: Ontologies for Generalised Property Graphs

Ego-centric Graph Pattern Census

SociaLite: Datalog extensions for efficient social network analysis

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