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SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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IEEE Transactions on Visualization and Computer Graphics
Computing Iceberg Queries Efficiently
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Graphs over time: densification laws, shrinking diameters and possible explanations
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ACM Computing Surveys (CSUR)
TopoLayout: Multilevel Graph Layout by Topological Features
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Less is More: Sparse Graph Mining with Compact Matrix Decomposition
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Graph summarization with bounded error
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CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficiently answering reachability queries on very large directed graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Graph OLAP: Towards Online Analytical Processing on Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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COCOON'99 Proceedings of the 5th annual international conference on Computing and combinatorics
TEDI: efficient shortest path query answering on graphs
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Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
A direct mining approach to efficient constrained graph pattern discovery
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Topological XML data cube construction
International Journal of Web Engineering and Technology
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We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called "TOLAP"), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these techniques are weaved. To the best of our knowledge, this is the first work to give a framework study for topological OLAP on information networks. Experimental results demonstrate both the effectiveness and efficiency of our proposed framework.