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Partitioning Complex Networks via Size-constrained Clustering [article]

Henning Meyerhenke and Peter Sanders and Christian Schulz
2014 arXiv   pre-print
The same algorithm that provides the size-constrained clusterings can also be used during uncoarsening as a fast and simple local search algorithm.  ...  More precisely, our algorithm provides graph coarsening by iteratively contracting size-constrained clusterings that are computed using a label propagation algorithm.  ...  Thus, guided by techniques used in complex network clustering, we have devised a new scheme for coarsening based on the contraction of clusters derived from size-constrained label propagation.  ... 
arXiv:1402.3281v2 fatcat:2ecuaiu2pnf7hpglaieo57g4ey

Fast Detection of Size-Constrained Communities in Large Networks [chapter]

Marek Ciglan, Kjetil Nørvåg
2010 Lecture Notes in Computer Science  
The algorithm is able to detect small-sized clusters independently of the network size.  ...  Our approach differs from others in the ability of constraining the size of communities being generated, a property important for a class of applications.  ...  constrained community detection no size constraint size constraint: 50 size constraint: 20 (b) Constraining the size of communities The members of the community are the following: Duncan J.  ... 
doi:10.1007/978-3-642-17616-6_10 fatcat:avxipr6ebfesxcbavso5nuidre

Balanced K-Means for Clustering [chapter]

Mikko I. Malinen, Pasi Fränti
2014 Lecture Notes in Computer Science  
This is a novel approach, and makes the assignment phase time complexity O(n 3 ), which is faster than the previous O(k 3.5 n 3.5 ) time linear programming used in constrained k-means.  ...  We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes.  ...  Time Complexity Time complexity of the assignment step in k-means is O(k · n). Constrained kmeans involves linear programming.  ... 
doi:10.1007/978-3-662-44415-3_4 fatcat:7uxac5azvnf2jmmau4nqza6nry

Improving the performance of algorithms to find communities in networks

Richard K. Darst, Zohar Nussinov, Santo Fortunato
2014 Physical Review E  
Many algorithms to detect communities in networks typically work without any information on the cluster structure to be found, as one has no a priori knowledge of it, in general.  ...  Not surprisingly, knowing some features of the unknown partition could help its identification, yielding an improvement of the performance of the method.  ...  INTRODUCTION Community structure is one of the most important features of complex networks.  ... 
doi:10.1103/physreve.89.032809 pmid:24730901 fatcat:qjj5ylkr7zcwhiooaghq2hzdz4

A Framework for Deep Constrained Clustering – Algorithms and Advances [article]

Hongjing Zhang, Sugato Basu, Ian Davidson
2019 arXiv   pre-print
The area of constrained clustering has been extensively explored by researchers and used by practitioners.  ...  Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations.  ...  Update network parameters based on total loss. end for for batch = 1 to NC do Calculate P via Eqn (4, 5) or T via Eqn(7).  ... 
arXiv:1901.10061v3 fatcat:jr4nazhvfzakpehsqc2gyxqi7m

Book announcement

1992 Discrete Applied Mathematics  
Chapter 6: Circuit Partitioning. Definitions and complexities (MuMway partitions. Bipartitions. Separators. Free partitions. Modeling hypergraphs with graphs).  ...  Graph partition and network flow (Maxflow techniques. Multicommodity-flow techniques). Partitioning as a nonlinear optimization problem (Partitioning by quadratic assignment.  ... 
doi:10.1016/0166-218x(92)90299-p fatcat:kkkpojidyvgifm6vpfwb5c2epi

On Approximate Balanced Bi-clustering [chapter]

Guoxuan Ma, Jiming Peng, Yu Wei
2005 Lecture Notes in Computer Science  
In particular, a novel and efficient heuristic, in which we first reformulate the constrained bi-clustering problem into a quadratic programming(QP) problem and then try to solve it by optimization technique  ...  A special case of the balanced bi-clustering, where the number of entities in each cluster is fixed, is discussed.  ...  In Section 3, we focus on fixed size bi-clustering problem. We also construct an algorithm via reformulating the problem to a Quadratic Programming(QP) problem.  ... 
doi:10.1007/11533719_67 fatcat:wdghtyfngngqzhjlfl5dfbmpzy

Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics [article]

Dalton Lunga, Jonathan Gerrand, Hsiuhan Lexie Yang, Christopher Layton, Robert Stewart
2019 arXiv   pre-print
The core contribution is partitioning massive amount of data based on the spectral and semantic characteristics for distributed imagery analysis.  ...  Each machine is connected to Network File System (NFS) storage via a single 10Gb Ethernet network connection.  ...  At the core of RESFlow is the concept of data distribution partitioning which is performed via efficient geometric based clustering and metric learning.  ... 
arXiv:1908.04383v1 fatcat:z33cgcot6fhwxpctb2b43phmqa

A Constrained Power Method for Community Detection in Complex Networks

Wenye Li
2014 Mathematical Problems in Engineering  
the resulting partitions.  ...  For an undirected complex network made up with vertices and edges, we developed a fast computing algorithm that divides vertices into different groups by maximizing the standard "modularity" measure of  ...  Unlike most other clustering models, which require a prior setting of partition numbers or group sizes [5] , maximizing the modularity score gives the partition number and the group size automatically  ... 
doi:10.1155/2014/804381 fatcat:ed4cvrrz6fegdovbnt6feybai4

Subsystem size optimization for efficient parallel restoration of power systems

Nuwan Ganganath, Chi-Tsun Cheng, Herbert H. C. Iu, Tyrone Fernando
2017 2017 IEEE International Symposium on Circuits and Systems (ISCAS)  
Existing network partitioning strategies for parallel restoration do not put control on the individual subsystem size.  ...  In this paper, we proposed a partitioning strategy that helps to accelerate the restoration process by minimizing the subsystem size differences.  ...  Lin et al. in [6] tackled the partitioning problem by exploiting complex networks properties in power systems.  ... 
doi:10.1109/iscas.2017.8050925 dblp:conf/iscas/GanganathCIF17 fatcat:u67prjg7dvhl7llxwlut3szsnm


Soumyalatha Naveen, Manjunath R Kounte, Mohammed Riyaz Ahmed
2021 IEEE Access  
Compared to DeepThings for 5 X 5 fused layer partitioning for five devices, our proposed model reduces communication size by ∼ 14.4% and inference latency by ∼16%.  ...  We propose decentralized heterogeneous edge clusters deployed with an optimized pre-trained yolov2 model.  ...  Then, the desired accuracy with moderate computations in the resource-constrained devices can be achieved by compressing the DNN models via Pruning.  ... 
doi:10.1109/access.2021.3131396 fatcat:gqllzrrvcjhjfjjcgb45gux5bu

Clustered Cell-Free Networking: A Graph Partitioning Approach [article]

Junyuan Wang, Lin Dai, Lu Yang, Bo Bai
2022 arXiv   pre-print
By formulating it as a bipartite graph partitioning problem, a rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current  ...  Therefore, it is of paramount importance to develop a flexible clustered cell-free networking scheme that can decompose the whole network into a number of weakly interfered small subnetworks operating  ...  Section II introduces the clustered cell-free network model and the rate-constrained network decomposition problem.  ... 
arXiv:2207.11641v1 fatcat:7g4r5v5xinhz3bic5tuwjzun2q

Partitioning of Distributed MIMO Systems Based on Overhead Considerations

Athanasios S. Lioumpas, Petros S. Bithas, Angeliki Alexiou
2013 IEEE Wireless Communications Letters  
Furthermore, in order to comply with practical requirements, the overhead subframe size is considered to be constrained.  ...  In this context, a novel formulation of constrained orthogonal partitioning is introduced as an elegant Knapsack optimization problem, which allows the derivation of quick and accurate solutions.  ...  Fig. 4 . 4 The constrained partitioning of the network as a function of the MAO and the CCT, for K = 9.  ... 
doi:10.1109/wcl.2013.072913.130449 fatcat:pbqvargvxnfslbm4dvlwaiz55e

A Declarative Approach to Constrained Community Detection [chapter]

Mohadeseh Ganji, James Bailey, Peter J. Stuckey
2017 Lecture Notes in Computer Science  
Community detection in the presence of prior information or preferences on solution properties is called semi-supervised or constrained community detection.  ...  technology for simultaneously modelling different objective functions such as modularity and a comprehensive range of constraint types including community level, instance level, definition based and complex  ...  In addition, capturing complex community level constraints requires a dual viewpoint to the partitioning problem which makes the modelling of constrained community detection different to constrained clustering  ... 
doi:10.1007/978-3-319-66158-2_31 fatcat:zqsivqx4erf73nqkhq7e5pnbli

Semi-supervised clustering algorithm for community structure detection in complex networks

Xiaoke Ma, Lin Gao, Xuerong Yong, Lidong Fu
2010 Physica A: Statistical Mechanics and its Applications  
Discovering a community structure is fundamental for uncovering the links between structure and function in complex networks.  ...  The algorithm is illustrated and compared with spectral clustering and NMF by using artificial examples and other classic real world networks.  ...  Thus, the time complexity of SNMF-SS is O(|V | 2 m 5 γ ), which shows that the computational complexity depends on the maximum iteration number, size of network and the number of clusters.  ... 
doi:10.1016/j.physa.2009.09.018 fatcat:yffebezpdfbszevuv3qotghgzy
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