GraphScope: parameter-free mining of large time-evolving graphs

@inproceedings{Sun2007GraphScopePM,
  title={GraphScope: parameter-free mining of large time-evolving graphs},
  author={Jimeng Sun and Christos Faloutsos and Spiros Papadimitriou and Philip S. Yu},
  booktitle={Knowledge Discovery and Data Mining},
  year={2007}
}
How can we find communities in dynamic networks of socialinteractions, such as who calls whom, who emails whom, or who sells to whom. [] Key Method Moreover, it is designed to operate on large graphs, in a streaming fashion. We demonstrate the efficiency and effectiveness of our GraphScope on real datasets from several diverse domains. In all cases it produces meaningful time-evolving patterns that agree with human intuition.
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632 Citations
Highly Influential Citations
42
Background Citations
235
Methods Citations
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Results Citations
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632 Citations

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References

SHOWING 1-10 OF 20 REFERENCES

AutoPart: Parameter-Free Graph Partitioning and Outlier Detection

This work proposes a novel way to group nodes, using information-theoretic principles to choose both the number of such groups and the mapping from nodes to groups, which is completely parameter-free, and also scales practically linearly with the problem size.

Graphs over time: densification laws, shrinking diameters and possible explanations

A new graph generator is provided, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study.

Online Analysis of Community Evolution in Data Streams

An online analytical processing framework is proposed which separates out online data summarization from offline exploratory querying and is a method which provides the ability to perform exploratory queries without compromising on the quality of the results.

Beyond streams and graphs: dynamic tensor analysis

The dynamic tensor analysis (DTA) method, and its variants are introduced, which provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations.

Fully automatic cross-associations

This work furnishes a clear, information-theoretic criterion to choose a good cross-association as well as its parameters, namely, the number of row and column groups, and provides scalable algorithms to approach the optimal.

Extracting Large-Scale Knowledge Bases from the Web

This paper develops novel algorithms for enumerating and organizing all web occurrences of certain subgraphs that are signatures of web phenomena such as tightly-focused topic communities, webrings, taxonomy trees, keiretsus, etc, and argues that these algorithms run efficiently in this model.

Multilevel k-way Partitioning Scheme for Irregular Graphs

In this paper, we present and study a class of graph partitioning algorithms that reduces the size of the graph by collapsing vertices and edges, we find ak-way partitioning of the smaller graph, and…

Compressing Large Boolean Matrices using Reordering Techniques

On power-law relationships of the Internet topology

These power-laws hold for three snapshots of the Internet, between November 1997 and December 1998, despite a 45% growth of its size during that period, and can be used to generate and select realistic topologies for simulation purposes.

Graph-based anomaly detection

This paper introduces two techniques for graph-based anomaly detection, and introduces a new method for calculating the regularity of a graph, with applications to anomaly detection.