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Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics
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<span title="2018-09-07">2018</span>
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arXiv
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In particular, our approach is a combination of shortest path (often called geodesic) recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs. ...
Herein, we present an approach to recover geometric and topological properties of a network with a small set of distance measurements. ...
The main contribution of this paper is a technique that combines VC based techniques with low-rank matrix completion, that allows the extraction of topology and geometric features of a network from a small ...
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Network Topology Mapping from Partial Virtual Coordinates and Graph Geodesics
[article]
<span title="2018-09-13">2018</span>
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arXiv
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Herein, we present an approach, based on the theory of low-rank matrix completion, to recover geometric properties of a network with only partial information about the VCs of nodes. ...
In particular, our approach is a combination of geodesic recovery concepts and low-rank matrix completion, generalized to the case of hop-distances in graphs. ...
Our approach combines network path sampling with matrix completion, and it is motivated by two observations. ...
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Low rank modeling of signed networks
<span title="">2012</span>
<i title="ACM Press">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12</a>
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Under such a model, the sign inference problem can be formulated as a low-rank matrix completion problem. ...
Most state-of-the-art approaches consider the notion of structural balance in signed networks, building inference algorithms based on information about links, triads, and cycles in the network. ...
Theorem 3 (Low Rank Structure of Signed Networks) The adjacency matrix A of a complete k-weakly balanced network has rank 1 if k ≤ 2, and has rank k for all k > 2. Proof. ...
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Prediction and Clustering in Signed Networks: A Local to Global Perspective
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<span title="2013-03-05">2013</span>
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For the low rank modeling approach, we provide theoretical performance guarantees via convex relaxations, scale it up to large problem sizes using a matrix factorization based algorithm, and provide extensive ...
Furthermore, motivated by the global structure of balanced networks, we propose an effective low rank modeling approach for both sign prediction and clustering. ...
We choose LR-SVP, LR-ALS, MOI-∞ and HOC-3 as representatives of the two approaches of low rank matrix completion, MOI-based, and HOC-based methods respectively. ...
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Table of Contents
<span title="">2021</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gkn2pu46ozb4tmkxczacnmtvkq" style="color: black;">IEEE Transactions on Signal Processing</a>
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Zeng, and M. A. Govoni Robust Low-Rank Tensor Completion Based on Tensor Ring Rank via p, -Norm . . . . . . . . . . . . . . . . X. P. Li and H. C. ...
A. Richards, S. Bagchi, and S. Sundaram Quantization Games on Social Networks and Language Evolution . . . . . . . . . . . . A. Mani, L. R. Varshney, and A. ...
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Community Detection in Partially Observable Social Networks
[article]
<span title="2021-04-16">2021</span>
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arXiv
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The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions. ...
To solve this problem, we introduce KroMFac, a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. ...
To reduce the complexity, we take advantage of the property that real-world social networks usually have a sparse and low-rank matrix structure [39] . ...
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Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information
<span title="2020-09-09">2020</span>
<i title="MDPI AG">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/63zsvf7vxzfznojpqgfvpyk2lu" style="color: black;">Algorithms</a>
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Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired. ...
Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network. ...
Examples include social network studies involving questionnaire-based random sampling designs ([1], Ch. 5.3), or experimental testing of suspected regulatory interactions among selected pairs of genes ...
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Sampling and Inference of Networked Dynamics using Log-Koopman Nonlinear Graph Fourier Transform
[article]
<span title="2020-10-16">2020</span>
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arXiv
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However, current polynomial Koopman operators result in a large sampling space due to: (i) the large size of polynomial based observables (O(N^2), N number of nodes in network), and (ii) not factoring ...
As a result, one can derive a sampling strategy via the linear evolution property of observable. ...
These approaches all provide attractive performances on sampling node compression and state recovery accuracy, under the important premise of a known and linear/linearized underlying model. ...
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Sparse and Compositionally Robust Inference of Microbial Ecological Networks
<span title="2015-05-07">2015</span>
<i title="Public Library of Science (PLoS)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ch57atmlprauhhbqdf7x4ytejm" style="color: black;">PLoS Computational Biology</a>
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SPIEC-EASI outperforms state-of-the-art methods in terms of edge recovery and network properties on realistic synthetic data under a variety of scenarios. ...
Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU interaction networks is severely under-powered, and additional ...
Acknowledgments We would like to thank Eric Alm and Jonathan Friedman for helpful discussions.
Author Contributions ...
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Blind identification of graph filters with sparse inputs
<span title="">2015</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fl5ojmjvcnfondavoy46ujryxm" style="color: black;">2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)</a>
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While the filtered graph signal is a bilinear function of x and h, y is also a linear combination of the entries of the rank-one matrix xh T . ...
This paper deals with the problem of blind graph filter identification, which finds applications in social and brain networks, to name a few. ...
the rank-one matrix xh T . ...
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Binary matrix completion with nonconvex regularizers
[article]
<span title="2019-04-08">2019</span>
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arXiv
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Many practical problems involve the recovery of a binary matrix from partial information, which makes the binary matrix completion (BMC) technique received increasing attention in machine learning. ...
Extensive experiments conducted on both synthetic and real-world data sets demonstrate the superiority of the proposed approach over other competing methods. ...
Related work In the last decade, based on the remarkable result of low-rank matrix completion [1] , a tremendous amount of work has focused on the problem, which enabled a burst of progress concerning ...
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Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks
<span title="2017-07-15">2017</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
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<span title="2016-10-26">2016</span> <span class="release-stage" >pre-print</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1610.08189v1">arXiv:1610.08189v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ynytdhiz3rddrif4zrjmj3wlpu">fatcat:ynytdhiz3rddrif4zrjmj3wlpu</a> </span>
Extensive tests on simulated and real stock quote data demonstrate the merits of the novel tensor-based approach. ...
The present paper advocates a novel SEM-based topology inference approach that entails factorization of a three-way tensor, constructed from the observed nodal data, using the well-known parallel factor ...
On the other hand, in the completely blind case, Algorithm 1 still results in a reliable estimate of the network topology with low edge identification error. ...
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Online Topology Inference from Streaming Stationary Graph Signals with Partial Connectivity Information
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<span title="2020-07-07">2020</span>
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arXiv
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Our goal is to track the (possibly) time-varying network topology, and effect memory and computational savings by processing the data on-the-fly as they are acquired. ...
Numerical tests illustrate the effectiveness of the proposed graph learning approach in adapting to streaming information and tracking changes in the sought dynamic network. ...
, COVID-19 incidence in different geographical regions linked via mobility or social graphs, and fake news that spread on online social networks. ...
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Blind identification of stochastic block models from dynamical observations
[article]
<span title="2019-12-03">2019</span>
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arXiv
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We consider a blind identification problem in which we aim to recover a statistical model of a network without knowledge of the network's edges, but based solely on nodal observations of a certain process ...
More concretely, we focus on observations that consist of single snapshots taken from multiple trajectories of a diffusive process that evolves over the unknown network. ...
to a network whose expected adjacency matrix has a low-rank block structure (rank k). ...
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Using LTI Dynamics to Identify the Influential Nodes in a Network
<span title="2016-12-28">2016</span>
<i title="Public Library of Science (PLoS)">
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spreader, the one that is capable of propagating information in the shortest time to a large portion of the network. ...
We utilize the system-theoretic approach considering the network as a Linear Time-Invariant system. By observing the system response we can quantify the importance of each node. ...
The matrix A determines the dynamics of the system and it can be obtained as a transpose of the adjacency matrix describing the network topology A ¼ A T adj [18, 19] . ...
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