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Showing papers on "Graph (abstract data type) published in 2014"


Journal ArticleDOI
TL;DR: Traces as mentioned in this paper is a graph isomorphism algorithm based on the refinement-individualization paradigm, and it is implemented in several of the key implementations of the program nauty.

1,602 citations


Proceedings ArticleDOI
06 Oct 2014
TL;DR: This paper introduces GraphX, an embedded graph processing framework built on top of Apache Spark, a widely used distributed dataflow system and demonstrates that GraphX achieves an order of magnitude performance gain over the base dataflow framework and matches the performance of specialized graph processing systems while enabling a wider range of computation.
Abstract: In pursuit of graph processing performance, the systems community has largely abandoned general-purpose distributed dataflow frameworks in favor of specialized graph processing systems that provide tailored programming abstractions and accelerate the execution of iterative graph algorithms. In this paper we argue that many of the advantages of specialized graph processing systems can be recovered in a modern general-purpose distributed dataflow system. We introduce GraphX, an embedded graph processing framework built on top of Apache Spark, a widely used distributed dataflow system. GraphX presents a familiar composable graph abstraction that is sufficient to express existing graph APIs, yet can be implemented using only a few basic dataflow operators (e.g., join, map, group-by). To achieve performance parity with specialized graph systems, GraphX recasts graph-specific optimizations as distributed join optimizations and materialized view maintenance. By leveraging advances in distributed dataflow frameworks, GraphX brings low-cost fault tolerance to graph processing. We evaluate GraphX on real workloads and demonstrate that GraphX achieves an order of magnitude performance gain over the base dataflow framework and matches the performance of specialized graph processing systems while enabling a wider range of computation.

1,027 citations


Book
David P. Woodruff1
14 Nov 2014
TL;DR: A survey of linear sketching algorithms for numeric allinear algebra can be found in this paper, where the authors consider least squares as well as robust regression problems, low rank approximation, and graph sparsification.
Abstract: This survey highlights the recent advances in algorithms for numericallinear algebra that have come from the technique of linear sketching,whereby given a matrix, one first compresses it to a much smaller matrixby multiplying it by a (usually) random matrix with certain properties.Much of the expensive computation can then be performed onthe smaller matrix, thereby accelerating the solution for the originalproblem. In this survey we consider least squares as well as robust regressionproblems, low rank approximation, and graph sparsification.We also discuss a number of variants of these problems. Finally, wediscuss the limitations of sketching methods.

584 citations


Journal ArticleDOI
TL;DR: A multimodal hypergraph learning-based sparse coding method is proposed for image click prediction, and the obtained click data is applied to the reranking of images, which shows the use of click prediction is beneficial to improving the performance of prominent graph-based image reranking algorithms.
Abstract: Image reranking is effective for improving the performance of a text-based image search. However, existing reranking algorithms are limited for two main reasons: 1) the textual meta-data associated with images is often mismatched with their actual visual content and 2) the extracted visual features do not accurately describe the semantic similarities between images. Recently, user click information has been used in image reranking, because clicks have been shown to more accurately describe the relevance of retrieved images to search queries. However, a critical problem for click-based methods is the lack of click data, since only a small number of web images have actually been clicked on by users. Therefore, we aim to solve this problem by predicting image clicks. We propose a multimodal hypergraph learning-based sparse coding method for image click prediction, and apply the obtained click data to the reranking of images. We adopt a hypergraph to build a group of manifolds, which explore the complementarity of different features through a group of weights. Unlike a graph that has an edge between two vertices, a hyperedge in a hypergraph connects a set of vertices, and helps preserve the local smoothness of the constructed sparse codes. An alternating optimization procedure is then performed, and the weights of different modalities and the sparse codes are simultaneously obtained. Finally, a voting strategy is used to describe the predicted click as a binary event (click or no click), from the images' corresponding sparse codes. Thorough empirical studies on a large-scale database including nearly 330 K images demonstrate the effectiveness of our approach for click prediction when compared with several other methods. Additional image reranking experiments on real-world data show the use of click prediction is beneficial to improving the performance of prominent graph-based image reranking algorithms.

502 citations


Proceedings ArticleDOI
06 Nov 2014
TL;DR: The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements and is tested and demonstrated using five indoor mapping sessions of a building.
Abstract: For large-scale and long-term simultaneous localization and mapping (SLAM), a robot has to deal with unknown initial positioning caused by either the kidnapped robot problem or multi-session mapping. This paper addresses these problems by tying the SLAM system with a global loop closure detection approach, which intrinsically handles these situations. However, online processing for global loop closure detection approaches is generally influenced by the size of the environment. The proposed graph-based SLAM system uses a memory management approach that only consider portions of the map to satisfy online processing requirements. The approach is tested and demonstrated using five indoor mapping sessions of a building using a robot equipped with a laser rangefinder and a Kinect.

368 citations


Journal ArticleDOI
TL;DR: In this paper, a review of the use of graph analysis in translational neuroscience has been presented, which provides practical indications to make sense of brain network analysis and contrast counterproductive attitudes.
Abstract: The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network properties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes.

342 citations


Journal ArticleDOI
TL;DR: This paper examines the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers.
Abstract: In this paper, we examine the main advances registered in the last ten years in Pattern Recognition methodologies based on graph matching and related techniques, analyzing more than 180 papers; the...

338 citations


Proceedings Article
21 Jun 2014
TL;DR: This work gives algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others, based upon a novel idea of observing correlations among features and using these to infer the underlying edge structure via a global graph recovery procedure.
Abstract: We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an n node multilayer network that has degree at most nγ for some γ < 1 and each edge has a random edge weight in [-1, 1]. Our algorithm learns almost all networks in this class with polynomial running time. The sample complexity is quadratic or cubic depending upon the details of the model. The algorithm uses layerwise learning. It is based upon a novel idea of observing correlations among features and using these to infer the underlying edge structure via a global graph recovery procedure. The analysis of the algorithm reveals interesting structure of neural nets with random edge weights.

313 citations


BookDOI
24 Sep 2014
Abstract: This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.

307 citations


Journal ArticleDOI
TL;DR: In this article, the importance of the lowest eigenvalue to economic and social outcomes was uncovered, and the authors combined potential games, optimization, and spectral graph theory to solve for all Nash and stable equilibria and applied the results to R&D, crime, and econometrics of peer effects.
Abstract: Geography and social links shape economic interactions. In industries, schools, and markets, the entire network determines outcomes. This paper analyzes a large class of games and obtains a striking result. Equilibria depend on a single network measure: the lowest eigenvalue. This paper is the first to uncover the importance of the lowest eigenvalue to economic and social outcomes. It captures how much the network amplifies agents' actions. The paper combines new tools--potential games, optimization, and spectral graph theory--to solve for all Nash and stable equilibria and applies the results to R&D, crime, and the econometrics of peer effects.

289 citations


Journal ArticleDOI
TL;DR: FIRM is introduced as an abstract framework, a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods and the so-called SLQG-FIRM, a concrete instantiation of FIRM that focuses on kinematic systems and then extends to dynamical systems by sampling in the equilibrium space.
Abstract: In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios.

Book
29 Oct 2014
TL;DR: In this article, a consensus region approach is proposed to design distributed cooperative protocols for multi-agent systems with complex dynamics, which decouples the design of the feedback gain matrices of the cooperative protocols from the communication graph and serves as a measure for the robustness of the protocols to variations of communication graph.
Abstract: Distributed controller design is generally a challenging task, especially for multi-agent systems with complex dynamics, due to the interconnected effect of the agent dynamics, the interaction graph among agents, and the cooperative control laws. Cooperative Control of Multi-Agent Systems: A Consensus Region Approach offers a systematic framework for designing distributed controllers for multi-agent systems with general linear agent dynamics, linear agent dynamics with uncertainties, and Lipschitz nonlinear agent dynamics. Beginning with an introduction to cooperative control and graph theory, this monograph: Explores the consensus control problem for continuous-time and discrete-time linear multi-agent systems Studies the H∞ and H2 consensus problems for linear multi-agent systems subject to external disturbances Designs distributed adaptive consensus protocols for continuous-time linear multi-agent systems Considers the distributed tracking control problem for linear multi-agent systems with a leader of nonzero control input Examines the distributed containment control problem for the case with multiple leaders Covers the robust cooperative control problem for multi-agent systems with linear nominal agent dynamics subject to heterogeneous matching uncertainties Discusses the global consensus problem for Lipschitz nonlinear multi-agent systems Cooperative Control of Multi-Agent Systems: A Consensus Region Approach provides a novel approach to designing distributed cooperative protocols for multi-agent systems with complex dynamics. The proposed consensus region decouples the design of the feedback gain matrices of the cooperative protocols from the communication graph and serves as a measure for the robustness of the protocols to variations of the communication graph. By exploiting the decoupling feature, adaptive cooperative protocols are presented that can be designed and implemented in a fully distributed fashion.

Journal ArticleDOI
TL;DR: This article presents a WSD algorithm based on random walks over large Lexical Knowledge Bases (LKB) that performs better than other graph-based methods when run on a graph built from WordNet and eXtended WordNet.
Abstract: Word Sense Disambiguation WSD systems automatically choose the intended meaning of a word in context. In this article we present a WSD algorithm based on random walks over large Lexical Knowledge Bases LKB. We show that our algorithm performs better than other graph-based methods when run on a graph built from WordNet and eXtended WordNet. Our algorithm and LKB combination compares favorably to other knowledge-based approaches in the literature that use similar knowledge on a variety of English data sets and a data set on Spanish. We include a detailed analysis of the factors that affect the algorithm. The algorithm and the LKBs used are publicly available, and the results easily reproducible.

Journal ArticleDOI
TL;DR: This work proves full identifiability in the case where all noise variables have the same variance: the directed acyclic graph can be recovered from the joint Gaussian distribution.
Abstract: SUMMARY We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model is a directed acyclic graph describing the relationships between the variables. In Gaussian structural equation models with linear functions, the graph can be identified from the joint distribution only up to Markov equivalence classes, assuming faithfulness. In this work, we prove full identifiability in the case where all noise variables have the same variance: the directed acyclic graph can be recovered from the joint Gaussian distribution. Our result has direct implications for causal inference: if the data follow a Gaussian structural equation model with equal error variances, then, assuming that all variables are observed, the causal structure can be inferred from observational data only. We propose a statistical method and an algorithm based on our theoretical findings.

Proceedings ArticleDOI
23 Jun 2014
TL;DR: CuSha is a CUDA-based graph processing framework that overcomes the above obstacle via use of two novel graph representations: G-Shards and Concatenated Windows.
Abstract: Vertex-centric graph processing is employed by many popular algorithms (eg, PageRank) due to its simplicity and efficient use of asynchronous parallelism The high compute power provided by SIMT architecture presents an opportunity for accelerating these algorithms using GPUs Prior works of graph processing on a GPU employ Compressed Sparse Row (CSR) form for its space-efficiency; however, CSR suffers from irregular memory accesses and GPU underutilization that limit its performance In this paper, we present CuSha, a CUDA-based graph processing framework that overcomes the above obstacle via use of two novel graph representations: G-Shards and Concatenated Windows (CW) G-Shards uses a concept recently introduced for non-GPU systems that organizes a graph into autonomous sets of ordered edges called shards CuSha's mapping of GPU hardware resources on to shards allows fully coalesced memory accesses CW is a novel representation that enhances the use of shards to achieve higher GPU utilization for processing sparse graphs Finally, CuSha fully utilizes the GPU power by processing multiple shards in parallel on GPU's streaming multiprocessors For ease of programming, CuSha allows the user to define the vertex-centric computation and plug it into its framework for parallel processing of large graphs Our experiments show that CuSha provides significant speedups over the state-of-the-art CSR-based virtual warp-centric method for processing graphs on GPUs

Journal ArticleDOI
TL;DR: In this paper, the Markov Stability, a time-parametrized function defined in terms of the statistical properties of a Markov process taking place on the graph, is introduced to find multi-scale community structure in large networks.
Abstract: Most methods proposed to uncover communities in complex networks rely on combinatorial graph properties. Usually an edge-counting quality function, such as modularity, is optimized over all partitions of the graph compared against a null random graph model. Here we introduce a systematic dynamical framework to design and analyze a wide variety of quality functions for community detection. The quality of a partition is measured by its Markov Stability, a time-parametrized function defined in terms of the statistical properties of a Markov process taking place on the graph. The Markov process provides a dynamical sweeping across all scales in the graph, and the time scale is an intrinsic parameter that uncovers communities at different resolutions. This dynamic-based community detection leads to a compound optimization, which favours communities of comparable centrality (as defined by the stationary distribution), and provides a unifying framework for spectral algorithms, as well as different heuristics for community detection, including versions of modularity and Potts model. Our dynamic framework creates a systematic link between different stochastic dynamics and their corresponding notions of optimal communities under distinct (node and edge) centralities. We show that the Markov Stability can be computed efficiently to find multi-scale community structure in large networks.

Journal ArticleDOI
TL;DR: The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links) and outperforms recent approaches for A/V classification.
Abstract: The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.

Proceedings ArticleDOI
22 Sep 2014
TL;DR: The proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640×480 point clouds, which to the best of the knowledge is much faster than state-of-the-art algorithms.
Abstract: Real-time plane extraction in 3D point clouds is crucial to many robotics applications. We present a novel algorithm for reliably detecting multiple planes in real time in organized point clouds obtained from devices such as Kinect sensors. By uniformly dividing such a point cloud into nonoverlapping groups of points in the image space, we first construct a graph whose node and edge represent a group of points and their neighborhood respectively. We then perform an agglomerative hierarchical clustering on this graph to systematically merge nodes belonging to the same plane until the plane fitting mean squared error exceeds a threshold. Finally we refine the extracted planes using pixel-wise region growing. Our experiments demonstrate that the proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640×480 point clouds, which to the best of our knowledge is much faster than state-of-the-art algorithms.

Journal ArticleDOI
TL;DR: A fast new operator-overloading method is presented that uses the expression template programming technique in C++ to provide a compile-time representation of each mathematical expression as a computational graph that can be efficiently traversed in either direction.
Abstract: Gradient-based optimization problems are encountered in many fields, but the associated task of differentiating large computer algorithms can be formidable. The operator-overloading approach to performing reverse-mode automatic differentiation is the most convenient for the user but current implementations are typically 10-35 times slower than the original algorithm. In this paper a fast new operator-overloading method is presented that uses the expression template programming technique in Cpp to provide a compile-time representation of each mathematical expression as a computational graph that can be efficiently traversed in either direction. Benchmarking with four different numerical algorithms shows this approach to be 2.6--9 times faster than current operator-overloading libraries, and 1.3--7.7 times more efficient in memory usage. It is typically less than 4 times the computational cost of the original algorithm, although poorer performance is found for all libraries in the case of simple loops containing no mathematical functions. An implementation is freely available in the Adept Cpp software library.

Proceedings ArticleDOI
23 Jun 2014
TL;DR: A novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed Undirected affinity graph and makes the tracker robust to the spatially close targets with similar appearance.
Abstract: Multi-target tracking is an interesting but challenging task in computer vision field. Most previous data association based methods merely consider the relationships (e.g. appearance and motion pattern similarities) between detections in local limited temporal domain, leading to their difficulties in handling long-term occlusion and distinguishing the spatially close targets with similar appearance in crowded scenes. In this paper, a novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed undirected affinity graph. The relationships between different detections across the spatiotemporal domain are considered in a high-order way, which makes the tracker robust to the spatially close targets with similar appearance. Meanwhile, the hierarchical design of the optimization process fuels our tracker to long-term occlusion with more robustness. Extensive experiments on various challenging datasets (i.e. PETS2009 dataset, ParkingLot), including both low and high density sequences, demonstrate that the proposed method performs favorably against the state-of-the-art methods.

Proceedings ArticleDOI
23 Jun 2014
TL;DR: The subspace segmentation problem is addressed by effectively constructing an exactly block-diagonal sample affinity matrix by proposing a graph Laplacian constraint based formulation, and developing an efficient stochastic subgradient algorithm for optimization.
Abstract: The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily desired for accurate sample clustering but is rather difficult to obtain. Most current state-of-the-art subspace segmentation methods (such as SSC[4] and LRR[12]) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix. In this work, we directly pursue the block-diagonal structure by proposing a graph Laplacian constraint based formulation, and then develop an efficient stochastic subgradient algorithm for optimization. Moreover, two new subspace segmentation methods, the block-diagonal SSC and LRR, are devised in this work. To the best of our knowledge, this is the first research attempt to explicitly pursue such a block-diagonal structure. Extensive experiments on face clustering, motion segmentation and graph construction for semi-supervised learning clearly demonstrate the superiority of our novelly proposed subspace segmentation methods.

Journal ArticleDOI
01 Aug 2014
TL;DR: This work develops an index, together with effective pruning rules and efficient search algorithms, and proposes techniques that use this infrastructure to answer aggregation queries and proposes an effective maintenance algorithm to handle online updates over RDF repositories.
Abstract: We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph based. We store RDF data as a large graph and also represent a SPARQL query as a query graph. Thus, the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.

Proceedings ArticleDOI
23 Jun 2014
TL;DR: A method for generating object segmentation proposals from groups of superpixels to improve efficiency by replacing exhaustive search and reach state-of-the-art with greatly reduced computational cost.
Abstract: We present a method for generating object segmentation proposals from groups of superpixels. The goal is to propose accurate segmentations for all objects of an image. The proposed object hypotheses can be used as input to object detection systems and thereby improve efficiency by replacing exhaustive search. The segmentations are generated in a class-independent manner and therefore the computational cost of the approach is independent of the number of object classes. Our approach combines both global and local search in the space of sets of superpixels. The local search is implemented by greedily merging adjacent pairs of superpixels to build a bottom-up segmentation hierarchy. The regions from such a hierarchy directly provide a part of our region proposals. The global search provides the other part by performing a set of graph cut segmentations on a superpixel graph obtained from an intermediate level of the hierarchy. The parameters of the graph cut problems are learnt in such a manner that they provide complementary sets of regions. Experiments with Pascal VOC images show that we reach state-of-the-art with greatly reduced computational cost.

Proceedings ArticleDOI
23 Jun 2014
TL;DR: This paper first parse the sentential descriptions into a semantic graph, which is then matched to visual concepts using a generalized bipartite matching algorithm, and learns the importance of each term using structure prediction.
Abstract: In this paper, we tackle the problem of retrieving videos using complex natural language queries. Towards this goal, we first parse the sentential descriptions into a semantic graph, which is then matched to visual concepts using a generalized bipartite matching algorithm. Our approach exploits object appearance, motion and spatial relations, and learns the importance of each term using structure prediction. We demonstrate the effectiveness of our approach on a new dataset designed for semantic search in the context of autonomous driving, which exhibits complex and highly dynamic scenes with many objects. We show that our approach is able to locate a major portion of the objects described in the query with high accuracy, and improve the relevance in video retrieval.

Proceedings ArticleDOI
23 Jun 2014
TL;DR: A new, efficient learning- and model-free approach for the segmentation of 3D point clouds into object parts that is comparable to state-of-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.
Abstract: The problem of how to arrive at an appropriate 3D-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotated data-sets. As an alternative to this, we present a new, efficient learning- and model-free approach for the segmentation of 3D point clouds into object parts. The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid. Edges in the graph are then classified as either convex or concave using a novel combination of simple criteria which operate on the local geometry of these patches. This way the graph is divided into locally convex connected subgraphs, which--with high accuracy--represent object parts. Additionally, we propose a novel depth dependent voxel grid to deal with the decreasing point-density at far distances in the point clouds. This improves segmentation, allowing the use of fixed parameters for vastly different scenes. The algorithm is straightforward to implement and requires no training data, while nevertheless producing results that are comparable to state-of-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.

Journal ArticleDOI
Mingsheng Long1, Jianmin Wang1, Guiguang Ding1, Dou Shen2, Qiang Yang3 
TL;DR: Graph Co-Regularized Transfer Learning (GTL) as mentioned in this paper proposes a general framework, referred to as graph co-regularized transfer learning, where various matrix factorization models can be incorporated.
Abstract: Transfer learning is established as an effective technology to leverage rich labeled data from some source domain to build an accurate classifier for the target domain. The basic assumption is that the input domains may share certain knowledge structure, which can be encoded into common latent factors and extracted by preserving important property of original data, e.g., statistical property and geometric structure. In this paper, we show that different properties of input data can be complementary to each other and exploring them simultaneously can make the learning model robust to the domain difference. We propose a general framework, referred to as Graph Co-Regularized Transfer Learning (GTL), where various matrix factorization models can be incorporated. Specifically, GTL aims to extract common latent factors for knowledge transfer by preserving the statistical property across domains, and simultaneously, refine the latent factors to alleviate negative transfer by preserving the geometric structure in each domain. Based on the framework, we propose two novel methods using NMF and NMTF, respectively. Extensive experiments verify that GTL can significantly outperform state-of-the-art learning methods on several public text and image datasets.

Journal ArticleDOI
TL;DR: The finite-time convergence of a nonlinear but continuous consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics is analyzed and it is shown that the proposed nonlinear consensus algorithm can guarantee infinite convergence if the directed switching interaction graph has a directed spanning tree at each time interval.

Journal ArticleDOI
TL;DR: A compositional framework for extending the controllability and observability of the factor networks to that of the composite network-of-networks via its symmetry and gramian structure is provided.
Abstract: The paper presents a system theoretic analysis framework for a network-of-networks, formed from smaller factor networks via graph Cartesian products We provide a compositional framework for extending the controllability and observability of the factor networks to that of the composite network-of-networks We then delve into the effectiveness of designing control and estimation algorithms for the composite network via its symmetry and gramian structure An example demonstrating the usefulness of our results in the context of social networks with a Cartesian product structure is then presented

Journal ArticleDOI
TL;DR: The block-diagonal mental image of the cavemen graph' is shown to be the wrong paradigm, and the SLASHBURN method to recursively split a graph into hubs and spokes connected only by the hubs is proposed.
Abstract: Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the ‘cavemen graph’, and compress them. We show that the block-diagonal mental image of the ‘cavemen graph’ is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the SlashBurn method to recursively split a graph into hubs and spokes connected only by the hubs. We also propose techniques to select the hubs and give an ordering to the spokes, in addition to the basic SlashBurn. We give theoretical analysis of the proposed hub selection methods. Our view point has several advantages: (a) it avoids the ‘no good cuts’ problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Through experiments, we show that SlashBurn consistently outperforms other methods for all data sets, resulting in better compression and faster running time. Moreover, we show that SlashBurn with the appropriate spokes ordering can further improve compression while hardly sacrificing the running time.

Journal ArticleDOI
TL;DR: In this article, the Sherrington-Kirkpatrick model with random $1$ couplings is programmed on the D-Wave Two annealer featuring 509 qubits interacting on a Chimera-type graph.
Abstract: The Sherrington-Kirkpatrick model with random $\pm1$ couplings is programmed on the D-Wave Two annealer featuring 509 qubits interacting on a Chimera-type graph. The performance of the optimizer compares and correlates to simulated annealing. When considering the effect of the static noise, which degrades the performance of the annealer, one can estimate an improvement on the comparative scaling of the two methods in favor of the D-Wave machine. The optimal choice of parameters of the embedding on the Chimera graph is shown to be associated to the emergence of the spin-glass critical temperature of the embedded problem.