On the representation and querying of sets of possible worlds
Selected papers of the workshop on Deductive database theory
ULDBs: databases with uncertainty and lineage
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Update semantics for incomplete databases
VLDB '85 Proceedings of the 11th international conference on Very Large Data Bases - Volume 11
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ERACER: a database approach for statistical inference and data cleaning
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
k-nearest neighbors in uncertain graphs
Proceedings of the VLDB Endowment
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
Graph cube: on warehousing and OLAP multidimensional networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A framework for SQL-Based mining of large graphs on relational databases
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Clustering Large Probabilistic Graphs
IEEE Transactions on Knowledge and Data Engineering
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We study group-summarization of probabilistic graphs that naturally arise in social networks, semistructured data, and other applications. Our proposed framework groups the nodes and edges of the graph based on a user selected set of node attributes. We present methods to compute useful graph aggregates without the need to create all of the possible graph-instances of the original probabilistic graph. Also, we present an algorithm for graph summarization based on pure relational (SQL) technology. We analyze our algorithm and practically evaluate its scalability using an extended Epinions dataset as well as synthetic datasets. The experimental results show that our algorithm produces compressed summary graphs in reasonable time.