From Balanced Graph Partitioning to Balanced Metric Labeling

<div>Localization is a fundamental task for optical internet</div><div>of underwater things (O-IoUT) to enable various applications</div><div>such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for OIoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor’s location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable</div><div>sensors.</div>

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Mathematics of networks

10.1093/oso/9780198805090.003.0006 ◽

2018 ◽

Author(s):

Mark Newman

Keyword(s):

Random Walks ◽

Adjacency Matrix ◽

Graph Partitioning ◽

Dynamic Networks ◽

Graph Laplacian ◽

Network Visualization ◽

Final Part ◽

Bipartite Networks ◽

Component Structure ◽

Basic Properties

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

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Reducing quantum annealing biases for solving the graph partitioning problem

Proceedings of the 18th ACM International Conference on Computing Frontiers ◽

10.1145/3457388.3458672 ◽

2021 ◽

Author(s):

Elijah Pelofske ◽

Georg Hahn ◽

Hristo N. Djidjev

Keyword(s):

Graph Partitioning ◽

Quantum Annealing ◽

Graph Partitioning Problem ◽

Partitioning Problem

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Application Driven Graph Partitioning

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data ◽

10.1145/3318464.3389745 ◽

2020 ◽

Cited By ~ 3

Author(s):

Wenfei Fan ◽

Ruochun Jin ◽

Muyang Liu ◽

Ping Lu ◽

Xiaojian Luo ◽

...

Keyword(s):

Graph Partitioning

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A computational-graph partitioning method for training memory-constrained DNNs

Parallel Computing ◽

10.1016/j.parco.2021.102792 ◽

2021 ◽

pp. 102792

Author(s):

Fareed Qararyah ◽

Mohamed Wahib ◽

Doğa Dikbayır ◽

Mehmet Esat Belviranli ◽

Didem Unat

Keyword(s):

Graph Partitioning ◽

Training Memory

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Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

Big Data and Cognitive Computing ◽

10.3390/bdcc5010014 ◽

2021 ◽

Vol 5 (1) ◽

pp. 14

Author(s):

Christos Makris ◽

Georgios Pispirigos

Keyword(s):

Social Networks ◽

Graph Partitioning ◽

Large Scale ◽

Real Life ◽

Information Networks ◽

Digital Marketing ◽

Partitioning Problems ◽

Iterative Solutions ◽

Community Extraction ◽

Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

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