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On power-law relationships of the Internet topology
TLDR
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. Expand
Efficient Similarity Search In Sequence Databases
TLDR
An indexing method for time sequences for processing similarity queries using R * -trees to index the sequences and efficiently answer similarity queries and provides experimental results which show that the method is superior to search based on sequential scanning. Expand
Cost-effective outbreak detection in networks
TLDR
This work exploits submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm and achieving speedups and savings in storage of several orders of magnitude. Expand
Graph evolution: Densification and shrinking diameters
TLDR
A new graph generator is provided, based on a forest fire spreading process that has a simple, intuitive justification, requires very few parameters, and produces graphs exhibiting the full range of properties observed both in prior work and in the present study. Expand
Graphs over time: densification laws, shrinking diameters and possible explanations
TLDR
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. Expand
Sampling from large graphs
TLDR
The best performing methods are the ones based on random-walks and "forest fire"; they match very accurately both static as well as evolutionary graph patterns, with sample sizes down to about 15% of the original graph. Expand
R-MAT: A Recursive Model for Graph Mining
TLDR
A simple, parsimonious model, the “recursive matrix” (R-MAT) model, which can quickly generate realistic graphs, capturing the essence of each graph in only a few parameters is proposed. Expand
Kronecker Graphs: An Approach to Modeling Networks
TLDR
Kronecker graphs naturally obey common network properties and it is rigorously proved that they do so, and KRONFIT, a fast and scalable algorithm for fitting the Kronecker graph generation model to large real networks, is presented. Expand
FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets
TLDR
A fast algorithm to map objects into points in some k-dimensional space (k is user-defined), such that the dis-similarities are preserved, and this method is introduced from pattern recognition, namely, Multi-Dimensional Scaling (MDS). Expand
Fast Random Walk with Restart and Its Applications
TLDR
The heart of the approach is to exploit two important properties shared by many real graphs: linear correlations and block- wise, community-like structure and exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman- Morrison lemma for matrix inversion. Expand
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