# SuperGraph Visualization

@article{Rodrigues2006SuperGraphV, title={SuperGraph Visualization}, author={Jos{\'e} Fernando Rodrigues and Agma J. M. Traina and Christos Faloutsos and Caetano Traina}, journal={Eighth IEEE International Symposium on Multimedia (ISM'06)}, year={2006}, pages={227-234} }

Given a large social or computer network, how can we visualize it, find patterns, outliers, communities? Although several graph visualization tools exist, they cannot handle large graphs with hundred thousand nodes and possibly million edges. Such graphs bring two challenges: interactive visualization demands prohibitive processing power and, even if we could interactively update the visualization, the user would be overwhelmed by the excessive number of graphical items. To cope with this… Expand

#### 19 Citations

Large Graph Analysis in the GMine System

- Computer Science
- IEEE Transactions on Knowledge and Data Engineering
- 2013

The visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally, and deals with the problem of tracing the connection aspects of a graph hierarchy with sub linear complexity. Expand

GViewer: GPU-Accelerated Graph Visualization and Mining

- Computer Science
- SocInfo
- 2011

In these experiments, the CPU-based solution takes more than half one hours on the CPU to layout a graph with 14.5 thousand vertexes, where N is the number of vertexes in the graph. Expand

Graph Aggregation : Application to Social Networks

- Computer Science
- HDSDA
- 2011

New measures to evaluate the quality of summaries based on an existing algorithm named k-SNAP that produces a summarized graph according to user-selected node attributes and relationships are proposed. Expand

Visual Statistics for Collections of Clustered Graphs

- Computer Science
- 2008 IEEE Pacific Visualization Symposium
- 2008

We propose a method to visually summarize collections of networks on which a clustering of the vertices is given. Our method allows for efficient comparison of individual networks, as well as for… Expand

Interactive Graph Summarization

- Computer Science
- Link Mining
- 2010

This chapter introduces database-style operations to summarize graphs, and describes the methods described, which provide an analogous functionality for large graph data sets. Expand

GMINESYS: Efficient approach to reduce storage area using LZW compression

- Computer Science
- 2015

This work presents a system that explores two new ideas to address scalability in large graph visualization, one to represent large graph using Gmine system and then second to reduce the storage area required to store the huge data. Expand

Efficient aggregation for graph summarization

- Computer Science
- SIGMOD Conference
- 2008

This paper introduces two database-style operations to summarize graphs, called SNAP and k-SNAP, that allow users to control the resolutions of summaries and provides the "drill-down" and "roll-up" abilities to navigate through summaries with different resolutions. Expand

Querying graph databases

- Computer Science
- 2008

A graph querying toolkit built on top of a commodity RDBMS that provides the support of various sophisticated graph query operations besides the simple ones like node/edge selection and path search, and introduces an aggregation method that produces summary graphs. Expand

Soft and Adaptive Aggregation of Heterogeneous Graphs with Heterogeneous Attributes

- Mathematics, Computer Science
- SWCS
- 2014

A new soft and adaptive approach to aggregate heterogeneous graphs i.e., composed of different node attributes and different relationship types using the definition of Rough Set Theory RST combined with Formal Concept Analysis FCA, the well known K-Medoids and the hierarchical clustering methods is developed. Expand

A Study to Handle Dynamic Graph Partitioning

- Computer Science
- 2014

The proposed work will analyze the large dynamic graph by partitioning it, and the system will update graph when multiple nodes and edges will be inserted or deleted at runtime. Expand

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