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Open AccessJournal ArticleDOI

Concentration Inequalities and Martingale Inequalities: A Survey

TLDR
In this article, a number of generalized and extended versions of concentration inequalities and martingale inequalities are examined for analyzing processes with quite general conditions as illustrated in an example for an infinite Polya process and web graphs.
Abstract
We examine a number of generalized and extended versions of concentration inequalities and martingale inequalities. These inequalities are effective for analyzing processes with quite general conditions as illustrated in an example for an infinite Polya process and web graphs.

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Proceedings ArticleDOI

Random graphs

TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
MonographDOI

Random Graphs and Complex Networks

TL;DR: This chapter explains why many real-world networks are small worlds and have large fluctuations in their degrees, and why Probability theory offers a highly effective way to deal with the complexity of networks, and leads us to consider random graphs.
Proceedings ArticleDOI

Influence Maximization in Near-Linear Time: A Martingale Approach

TL;DR: The proposed influence maximization algorithm is a set of estimation techniques based on martingales, a classic statistical tool that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency.
Posted Content

Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks

TL;DR: SSA and D-SSA as mentioned in this paper are two sampling frameworks for IM-based viral marketing problems, which are up to 1200 times faster than the SIGMOD'15 best method, IMM, while providing the same $(1-1/e-\epsilon) approximation guarantee.
ReportDOI

An Online Algorithm for Maximizing Submodular Functions

TL;DR: An algorithm for solving a broad class of online resource allocation problems, applied in environments where abstract jobs arrive one at a time, and one can complete the jobs by investing time in a number of abstract activities, according to some schedule.
References
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Journal ArticleDOI

Emergence of Scaling in Random Networks

TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Journal ArticleDOI

Statistical mechanics of complex networks

TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Proceedings ArticleDOI

Random graphs

TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.