The hypernode model and its associated query language
JCIT Proceedings of the fifth Jerusalem conference on Information technology
An object-oriented data model formalised through hypergraphs
Data & Knowledge Engineering
Gram: a graph data model and query languages
ECHT '92 Proceedings of the ACM conference on Hypertext
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The object data standard: ODMG 3.0
The object data standard: ODMG 3.0
A relational model of data for large shared data banks
Communications of the ACM
Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
What You Always Wanted to Know About Datalog (And Never Dared to Ask)
IEEE Transactions on Knowledge and Data Engineering
A Graph-Oriented Object Database Model
IEEE Transactions on Knowledge and Data Engineering
Typing Graph-Manipulation Operations
ICDT '03 Proceedings of the 9th International Conference on Database Theory
GraphDB: Modeling and Querying Graphs in Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A Graph Query Language and Its Query Processing
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A query language for biological networks
Bioinformatics
Advances in Mining Graphs, Trees and Sequences (Frontiers in Artificial Intelligence and Applications, Vol. 124) (Frontiers in Artificial Intelligence and Applications)
Bigtable: a distributed storage system for structured data
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Dex: high-performance exploration on large graphs for information retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Survey of graph database models
ACM Computing Surveys (CSUR)
Graphs-at-a-time: query language and access methods for graph databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Anti-monotonic Overlap-Graph Support Measures
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Towards bisociative knowledge discovery
Bisociative Knowledge Discovery
Bisociative Knowledge Discovery
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One of the key steps in data analysis is the exploration of data. For traditional relational data, this process is facilitated by relational database management systems and the aggregates and rankings they can compute. However, for the exploration of graph data, relational databases may not be most practical and scalable. Many tasks related to exploration of information networks involve computation and analysis of connections (e.g. paths) between concepts. Traditional relational databases offer no specific support for performing such tasks. For instance, a statistic such as the shortest path between two given nodes cannot be computed by a relational database. Surprisingly, tools for querying graph and network databases are much less well developed than for relational data, and only recently an increasing number of studies are devoted to graph or network databases. Our position is that the development of such graph databases is important both to make basic graph mining easier and to prepare data for more complex types of analysis. In this chapter we present the BiQL data model for representing and manipulating information networks. The BiQL data model consists of two parts: a data model describing objects, link, domains and networks, and a query language describing basic network manipulations. The main focus here lies on data preparation and data analysis, and less on data mining or knowledge discovery tasks directly.