Open AccessPosted Content
Compressive Network Analysis
TL;DR: This paper presents a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing, and considers the network clique detection problem and shows connections between the formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces.
read more
Abstract: Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Random graphs
Alan Frieze
- 22 Jan 2006
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.
9.5K
•Proceedings Article
Graphlet decomposition of a weighted network
Hossein Azari Soufiani,Edoardo M. Airoldi +1 more
- 21 Mar 2012
TL;DR: In this article, the graphlet decomposition of a weighted network is introduced, which encodes a notion of social information based on social structure, and a scalable algorithm combines EM with Bron-Kerbosch in a novel fashion for estimating the parameters of the model underlying graphlets using one network sample.
•Proceedings Article
Correlated compressive sensing for networked data
Tianlin Shi,Da Tang,Liwen Xu,Thomas Moscibroda +3 more
- 23 Jul 2014
TL;DR: This paper presents a novel correlated compressive sensing method called CorrCS for networked data, naturally extending Bayesian compression sensing, that extracts correlations from network topology and encode them into a graphical model as prior.
Semantic positioning via structured sparsity models
Giuseppe Destino,Davide Macagnano +1 more
- 06 Mar 2014
TL;DR: This paper develops a structured sparsity model based on the notion of discrete Radon transforms on homogeneous space in order to construct mappings from events to actions and from actions to semantic locations and proposes algorithms for human activity detection and semantic positioning.
3
•Dissertation
Dragon-Lab, network states detection and identification framework: Performance investigation
Alexandru Calu
- 12 Sep 2011
TL;DR: The research aims to use available data to pinpoint the source location of instabilities on the Internet paths, in essence to find the problem hop on the path by IP by combining reconstructions algorithms from the Compressive Sensing domain with Dragon-Lab knowledge on instabilities.
References
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
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.
39.1K
Community structure in social and biological networks
Michelle Girvan,Mark Newman +1 more
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
- 17 Jun 1997
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.