# StructMatrix: Large-Scale Visualization of Graphs by Means of Structure Detection and Dense Matrices

@article{Gualdron2015StructMatrixLV, title={StructMatrix: Large-Scale Visualization of Graphs by Means of Structure Detection and Dense Matrices}, author={Hugo Gualdron and Robson Leonardo Ferreira Cordeiro and Jos{\'e} Fernando Rodrigues}, journal={2015 IEEE International Conference on Data Mining Workshop (ICDMW)}, year={2015}, pages={493-500} }

Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at large-scale. Hence, this paper describes StructMatrix, a…

## Figures, Tables, and Topics from this paper

## 3 Citations

Visualization of large graphs and adjacency matrices

- Computer Science
- 2018

This survey discusses new approaches to the visualization of large graphs using adjacency matrices and gives examples of applications where these approaches are used, and describes various types of patterns arising when adjacencies corresponding to modern networks are ordered.

PGX.UI: Visual Construction and Exploration of Large Property Graphs

- Computer ScienceVISIGRAPP
- 2017

The concept of a graph construction time line that keeps track of changes and provides branching and merging, in a version control like fashion is introduced and a tool that visually guides users through the graph construction and exploration process is presented.

Analyzing data flow diagrams by combination of formal methods and visualization techniques

- Computer ScienceJ. Vis. Lang. Comput.
- 2018

A visual system is introduced to assist developers to analyze and compare the systems by combination of formal methods and visualization techniques.

## References

SHOWING 1-10 OF 20 REFERENCES

NodeTrix: a Hybrid Visualization of Social Networks

- Computer Science, MedicineIEEE Transactions on Visualization and Computer Graphics
- 2007

NodeTrix is presented, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities.

EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs

- Computer Science2009 IEEE International Conference on Data Mining Workshops
- 2009

It is found that the singular vectors of these graphs exhibit a striking EigenSpokes pattern wherein, when plotted against each other, they have clear, separate lines that often neatly align along specific axes (hence the term "spokes").

ZAME: Interactive Large-Scale Graph Visualization

- Computer Science2008 IEEE Pacific Visualization Symposium
- 2008

Using ZAME, the zoomable adjacency matrix explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges, the entire French Wikipedia is explored with interactive performance on standard consumer-level computer hardware.

Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data

- Computer Science, MedicineIEEE Transactions on Visualization and Computer Graphics
- 2006

This paper presents a new method for visualizing compound graphs based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together and discusses the results based on an informal evaluation provided by potential users of such visualizations.

VOG: Summarizing and Understanding Large Graphs

- Computer Science, PhysicsSDM
- 2014

The main ideas are to construct a "vocabulary" of sub graph-types that often occur in real graphs, and from a set of subgraphs, find the most succinct description of a graph in terms of this vocabulary.

Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining

- Mathematics, Computer Science2011 IEEE 11th International Conference on Data Mining
- 2011

This work proposes the Slash Burn method (burn the hubs, and slash the remaining graph into smaller connected components), which avoids the `no good cuts' problem, gives better compression, and leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools.

A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations

- Computer ScienceIEEE Symposium on Information Visualization
- 2004

It is shown that when graphs are bigger than twenty vertices, the matrix-based visualization performs better than node- link diagrams on most tasks, and only path finding is consistently in favor of node-link diagrams throughout the evaluation.

MatLink: Enhanced Matrix Visualization for Analyzing Social Networks

- Computer ScienceINTERACT
- 2007

This article presents MatLink, a hybrid representation with links overlaid on the borders of a matrix and dynamic topological feedback as the pointer moves that showed significant advantages for most tasks, especially path-related ones where standard matrices are weak.

Statistical properties of community structure in large social and information networks

- Computer ScienceWWW
- 2008

It is found that a generative model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community structure similar to that observed in nearly every network dataset examined.

PEGASUS: A Peta-Scale Graph Mining System Implementation and Observations

- Computer Science2009 Ninth IEEE International Conference on Data Mining
- 2009

This paper describes PEGASUS, an open source Peta Graph Mining library which performs typical graph mining tasks such as computing the diameter of the graph, computing the radius of each node and finding the connected components, and describes a very important primitive for PEGasUS, called GIM-V (Generalized Iterated Matrix-Vector multiplication).