Graph Partitioning Algorithm Research Articles

Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening

Objective. Music, being a multifaceted stimulus evolving at multiple timescales, modulates brain function in a manifold way that encompasses not only the distinct stages of auditory perception, but also higher cognitive processes like memory and appraisal. Network theory is apparently a promising approach to describe the functional reorganization of brain oscillatory dynamics during music listening. However, the music induced changes have so far been examined within the functional boundaries of isolated brain rhythms. Approach. Using naturalistic music, we detected the functional segregation patterns associated with different cortical rhythms, as these were reflected in the surface electroencephalography (EEG) measurements. The emerged structure was compared across frequency bands to quantify the interplay among rhythms. It was also contrasted against the structure from the rest and noise listening conditions to reveal the specific components stemming from music listening. Our methodology includes an efficient graph-partitioning algorithm, which is further utilized for mining prototypical modular patterns, and a novel algorithmic procedure for identifying ‘switching nodes’ (i.e. recording sites) that consistently change module during music listening. Main results. Our results suggest the multiplex character of the music-induced functional reorganization and particularly indicate the dependence between the networks reconstructed from the δ and βH rhythms. This dependence is further justified within the framework of nested neural oscillations and fits perfectly within the context of recently introduced cortical entrainment to music. Significance. Complying with the contemporary trends towards a multi-scale examination of the brain network organization, our approach specifies the form of neural coordination among rhythms during music listening. Considering its computational efficiency, and in conjunction with the flexibility of in situ electroencephalography, it may lead to novel assistive tools for real-life applications.

Stabilized Bordered Block Diagonal Form for Solving Nonlinear Magnetic Field Problems

This paper presents an adapted multilevel Newton–Raphson method based on a stabilized bordered block diagonal form of Jacobian matrix for solving the problems of nonlinear electromagnetic field. This algorithm can reduce the computing time with parallel computation. For analyzing large-scale engineering problems using numerical methods, parallel computation is an efficient approach. Graph partitioning algorithms in the numerical parallel computation have important theoretical significance and practical applications. The proposed method is based on a parallel algorithm, and is quite competent for solving large-scale nonlinear problems for possible reduced iteration steps, which has been demonstrated by the numerical experimental results.

Parallel Schur Complement Techniques Based on Multiprojection Methods

The simulation of several physical phenomena, arising from a wide class of scientific fields, requires solving large sparse linear systems effectively. In order to exploit appropriately the available supercomputing infrastructures, designing efficient and scalable solvers for large sparse linear systems is necessary. A hybrid parallel algebraic iterative method based on the Schur complement method and multiprojection techniques that utilizes semiaggregated subdomains is proposed. The graph corresponding to the coefficient matrix of the sparse linear system is partitioned by a graph partitioning algorithm and the Schur complement problem is solved by a preconditioned Krylov subspace method. The preconditioning scheme is based on domain decomposition and the resulting subdomains consist of fine and aggregated components from the inner boundaries. Moreover, two variants for approximating the local Schur complements, derived from the semiaggregation procedure, are proposed in order to improve memory requirements and preprocessing time. The proposed method is suitable for distributed memory systems with multicore nodes and has improved convergence behavior for a large number of workstations. Numerical and comparative results concerning the applicability, the convergence behavior, and the parallel performance of the proposed scheme are given.

A comparative study of graph partitioning algorithms for simulation of information spread in a multi-community landscape

When large complex networks are under consideration, one needs to exploit parallel computations to be able to get the result of simulation in time. To maintain the efficiency of calculations, different graph partitioning and dynamic load balancing algorithm exist. In this study, we explore the applicability of existing methods for special types of graphs with represent multi-community landscape of online social networks (OSNs). Compared to friendship networks, community-user graphs have super-spreaders and extremely small diameter hampering the achievement of balanced distributions of vertices between computational processes. We suggest a model of a multi-community networks and test the quality of solutions provided by different graph partitioning algorithms for synthetic multi-community graphs. The results show that METIS provides more balanced partitions than depth-first traversal heuristic in terms of the edge-cut while sizes of subnetworks are balanced in both cases.

ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications

In this article we propose a new paradigm of resource-efficient edge computing for the emerging intelligent IoT applications such as flying ad hoc networks for precision agriculture, e-health, and smart homes. We devise a resource-efficient edge computing scheme such that an intelligent IoT device user can well support its computationally intensive task by proper task offloading across the local device, nearby helper device, and the edge cloud in proximity. Different from existing studies for mobile computation offloading, we explore the novel perspective of resource efficiency and devise an efficient computation offloading mechanism consisting of a delay-aware task graph partition algorithm and an optimal virtual machine selection method in order to minimize an intelligent IoT device's edge resource occupancy and meanwhile satisfy its QoS requirement. Performance evaluation corroborates the effectiveness and superior performance of the proposed resource-efficient edge computing scheme.

DyBGP: A Dynamic-Balanced Algorithm for Graph Partitioning Based on Heuristic Strategies

DyBGP: A Dynamic-Balanced Algorithm for Graph Partitioning Based on Heuristic Strategies

A robust three-stage approach to large-scale urban scene recognition

To obtain the ultimate high-level description of urban scenes, we propose a three-stage approach to recognizing the 3D reconstructed scene with efficient representations. First, we develop a joint semantic labeling method to obtain a semantic labeling of the triangular mesh-based representation by exploiting both image features and geometric features. The labeling is formulated over a conditional random field (CRF) that incorporates local spacial smoothness and multi-view consistency. Then, based on the labeled reconstructed meshes, we refine the man-made object segmentation in the recomposed global orthographic map with a graph partition algorithm, and propagate the coherent segmentation to the entire 3D meshes. Finally, we propose to generate a compact, abstracted geometric representation for each man-made object which is more visually appealing than the original cluttered models. This abstraction algorithm also leverages CRF formation to partition building footprints into minimal sets of structural linear features which are then used to construct profiles for large-scale scenes. The proposed recognition approach is able to robustly handle reconstructions with poor geometry and connectivity, thanks to the higher order CRF formulations which impose the ubiquitous regularity priors in urban scenes. Each stage performs an individual and uncoupling task. The intensive experiments have demonstrated the superior performance of our approach in robustness, accuracy and applicability.

A graph partitioning algorithm for leak detection in water distribution networks

Urban water distribution networks (WDNs) are large scale complex systems with limited instrumentation. Due to aging and poor maintenance, significant loss of water can occur through leaks. We present a method for leak detection in WDNs using repeated water balance and minimal use of additional off-line flow measurements. A multi-stage graph partitioning approach is used to determine where the off-line flow measurements are to be made, with the objective of minimizing the measurement cost. The graph partitioning problem is formulated and solved as a multi-objective mixed integer linear program (MILP). We further derive an approximate method inspired by spectral graph bisection to solve the MILP, which is suitable for very large scale networks. The proposed methods are tested on large scale benchmark networks, and the results indicate that on average, flows in less than 3% of the pipes need to be measured to identify the leaky pipe or joint.

Partitioning large-scale artificial society on distributed cluster with statistical movement graph

ABSTRACTDistributed agent-based simulation is a popular method to realize computational experiment on large-scale artificial society. Meanwhile, the partitioning strategy of the artificial society models among hosts is playing an essential role for simulation engine to offer high execution efficiency as it has great impact on the communication overheads and computational load-balancing during simulation. Aiming at the problem, we firstly analyze the execution and scheduling process of agents during simulation and model it as wide-sense cyclostationary random process. Then, a static statistical partitioning model is proposed to obtain the optimal partitioning strategy with minimum average communication cost and load imbalance factor. To solve the static statistical partitioning model, this paper turns it into a graph-partitioning problem. A statistical movement graph-based partitioning algorithm is then devised which generates task graph model by mining the statistical movement information from initialization data of simulation model. In the experiments, two other popular partitioning methods are used to evaluate the performance of proposed graph-partitioning algorithm. Furthermore, this paper compares the graph-partitioning performance under different task graph model. The results indicate that our proposed statistical movement graph-based static partitioning method outperforms all other methods in reducing the communication overhead while satisfying the load balance constraint.

A feasible graph partition framework for parallel computing of big graph

With the emerging of large scale complex networks, graph computation, such as community detection, meets new technology challenges of extremely large computational cost. In order to deal with these challenges, the parallelism is becoming necessary. Graph partition is a fundamental problem of parallel computing for big graph data. The challenges of graph partition include large numbers of communications between partitions, extreme replication of vertices, and unbalanced partition. In this paper, we propose a feasible graph partition framework for parallel computing of big graph. The framework is based on an objective optimization problem to reduce the bandwidth, memory and storage cost on condition that the load balance could be guaranteed. In this framework, three greedy graph partition algorithms are proposed. By running the algorithms on the different kinds of graphs, including real-world graphs and synthetic graphs, the experimental results show that our algorithms surpass the state of the art heuristic algorithms. For example, running time is reduced more than 21.56% and the communication cost is decreased by more than 17.90% for weighted graph. The adequate experiments verify that our algorithms are capable of solving the problem of graph partition with different needs.

A density-based approach for detecting complexes in weighted PPI networks by semantic similarity.

Protein complex detection in PPI networks plays an important role in analyzing biological processes. A new algorithm-DBGPWN-is proposed for predicting complexes in PPI networks. Firstly, a method based on gene ontology is used to measure semantic similarities between interacted proteins, and the similarity values are used as their weights. Then, a density-based graph partitioning algorithm is developed to find clusters in the weighted PPI networks, and the identified ones are considered to be dense and similar. Experimental results demonstrate that our approach achieves good performance as compared with such algorithms as MCL, CMC, MCODE, RNSC, CORE, ClusterOne and FGN.

Incremental Graph Partitioning Based on MapReduce

Graph partitioning is widely used in many diverse domains. Incremental graph is a member of Graph which evolves with time. Traditional static graph partitioning methods can not meet the requirement with the incremental graph partitioning due to expensive computational cost and long off-line processing time. In this paper, we propose a distributed incremental graph partitioning algorithm to partition the incremental Graphs into smaller components with Hadoop. Our method considers four different modification events of incremental graph partitioning. Moreover, we evaluate our method in Metis and the experimental results show our method is more effective in terms of different measurements than baselines.

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Special issue on Big Data Security and Intelligent Data in Clouds (<scp>BDS‐IDC</scp>)

A PCI planning algorithm for jointly reducing reference signal collisions in LTE uplink and downlink

In this paper, a novel automatic planning method is proposed for allocating Physical Cell Identifiers (PCI) to cells in a Long Term Evolution (LTE) system based on handover and cell load measurements. The method aims at avoiding PCI collision and confusion problems, while reducing Reference Signal (RS) collisions between neighbor cells. For this purpose, the problem is formulated to consider RS collisions both in DownLink (DL) and UpLink (UL). Then, a classical graph partitioning algorithm is adapted to solve the graph coloring problem behind PCI planning. The considered algorithm is the multi-level version of the Fiduccia and Mattheyses local refinement algorithm. Performance assessment is carried out on graphs constructed from data collected in live LTE network. Results show that the proposed algorithms can avoid PCI collision and confusion problems, while reducing DL RS collisions and almost eliminating UL RS collisions compared to a random plan.

POSTER

Large-scale graphs are becoming critical in various domains such as social network, scientific application, knowledge discovery, and even system software, etc. Many of those use cases require large-scale high-performance graph databases, which are designed for serving continuous updates from the clients, and at the same time, answering complex queries towards the current graph in an on-line manner. Those operations in graph databases, also referred as OLTP (online transaction processing) operations, need specific design and implementation in graph partitioning algorithms. In this study, we designed an incremental online graph partitioning (IOGP), optimized for OLTP workloads. It is designed to achieve better locality, generate balanced partitions, and increase the parallelism for accessing hotspots of the graph. Our evaluation results on both real world and synthetic graphs in both simulation and real system confirm a better performance on graph queries (as much as 2X) with small overheads during graph insertion (less than 10%).

Partitioning dynamic graph asynchronously with distributed FENNEL

Graph partitioning is important in distributed graph processing. Classical method such as METIS works well on relatively small graphs, but hard to scale for huge, dynamic graphs. Streaming graph partitioning algorithms overcome this issue by processing those graphs as streams. Among these algorithms, FENNEL achieves better edge cut ratio, even close to METIS, but consumes less memory and is significantly faster. On the other hand, graph partitioning may also benefit from distributed graph processing. However, to deploy FENNEL on a cluster, it is important to avoid quality loss and keep efficiency high. The direct implementation of this idea yields a synchronous model and a star-shaped network, which limits both scalability and efficiency. Targeting these two problems, we propose an asynchronous model, combined with a dedicated tree-shaped map-reduce network which is prevail in systems such as Apache Hadoop and Spark, to form AsyncFENNEL (asynchronous FENNEL). We theoretically prove that, the impact on partition quality brought by asynchronous model can be kept as minimal. We test AsyncFENNEL with various synthetic and real-world graphs, the comparison between synchronous and asynchronous models shows that, for streamed natural graphs, AsyncFENNEL can improve performance significantly (above 300% with 8 workers/partitions) with negligible loss on edge cut ratio. However, more worker nodes will introduce a heavier network traffic and reduce efficiency. The proposed tree-shaped map-reduce network can mitigate that impact and increase the performance in that case.

Refficientlib: An Efficient Load-Rebalanced Adaptive Mesh Refinement Algorithm for High-Performance Computational Physics Meshes

In this paper we present a novel algorithm for adaptive mesh refinement in computational physics meshes in a distributed memory parallel setting. The proposed method is developed for nodally based parallel domain partitions where the nodes of the mesh belong to a single processor, whereas the elements can belong to multiple processors. Some of the main features of the algorithm presented in this paper are its capability of handling multiple types of elements in two and three dimensions (triangular, quadrilateral, tetrahedral, and hexahedral), the small amount of memory required per processor, and the parallel scalability up to thousands of processors. The presented algorithm is also capable of dealing with nonbalanced hierarchical refinement, where multirefinement level jumps are possible between neighbor elements. An algorithm for dealing with load rebalancing is also presented, which allows us to move the hierarchical data structure between processors so that load unbalancing is kept below an acceptable level at all times during the simulation. A particular feature of the proposed algorithm is that arbitrary renumbering algorithms can be used in the load rebalancing step, including both graph partitioning and space-filling renumbering algorithms. The presented algorithm is packed in the Fortran 2003 object oriented library \textttRefficientLib, whose interface calls which allow it to be used from any computational physics code are summarized. Finally, numerical experiments illustrating the performance and scalability of the algorithm are presented.

Improved Cheeger's Inequality and Analysis of Local Graph Partitioning using Vertex Expansion and Expansion Profile

We prove two generalizations of the Cheeger's inequality. The first generalization relates the second eigenvalue to the edge expansion and the vertex expansion of the graph $G$, $\lambda_2 = \Omega( \phi^V(G) \phi(G) )$, where $\phi^V(G)$ denotes the robust vertex expansion of $G$ and $\phi(G)$ denotes the edge expansion of $G$. The second generalization relates the second eigenvalue to the edge expansion and the expansion profile of $G$, for all $k \geq 2$, $ \lambda_2 = \Omega( \phi_k(G) \phi(G) / k )$, where $\phi_k(G)$ denotes the $k$-way expansion of $G$. These show that the spectral partitioning algorithm has better performance guarantees when $\phi^V(G)$ is large (e.g., planted random instances) or $\phi_k(G)$ is large (instances with few disjoint nonexpanding sets). Both bounds are tight up to a constant factor. Our approach is based on a method to analyze solutions of Laplacian systems, and this allows us to extend the results to local graph partitioning algorithms. In particular, we show that ou...

Minimization of Communication Cost between Controller Domains with Graph Partitioning Algorithms

Software Defined Networking is an emerging paradigm in Internet communication world that increases the flexibility of today’s networks by decoupling control plane and data plane of the network devices. The fundamental aim is to centralize the control and reduce the complexity of the networks. The communication medium between control and data plane is through OpenFlow protocol, an open standard network protocol designed to manage the network traffic by software programs. To increase the scalability and flexibility of controllers the OpenFlow controllers are distributed based on location and network types. However, most critical issue is minimizing the communication cost between the controller domains. In this paper, two graph partitioning algorithms such as Fiduccia-Matthyses algorithm and Kernighan-Lin algorithm are used to minimize the communication cost between distributed OpenFlow controller domains. The implementation of the algorithms is under matlab simulation environment. The methodology used for the proposed algorithms is to interchange the elements from one domain to other domain to calculate the gain. The simulated results show that Kernighan-Lin algorithm minimizes more communication cost rather than the Fiduccia-Matthyses algorithm.

LBMIC: communication-aware load balancing in distributed ASMs with evolving social networks

Multi-agent-based simulation for artificial stock market (ASM) is an important method in behavioural finance. The social network in ASM will influence the coordination and decision making of the intelligent agents. To improve the performance of an ASM with evolving social networks in a distributed computing environment, the computational load balancing and inter-nodes communication should be considered jointly. This paper proposes a scheduling algorithm called LBMIC to partition the agents onto different computing nodes while keeping the degree of load imbalance lower than a given threshold with minimized inter-nodes communication between agents. LBMIC models the scheduling into a graph partitioning problem and uses the multi-level graph partitioning algorithm to achieve an efficient scheduling. When the network evolves, LBMIC refines the partitioning by migrating parts of the agents. The experiments indicate that LBMIC can efficiently improve the performance of communication-intensive ASMs by both initial partitioning and refining partitioning.