A study on reliability in graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated.

In this paper we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be.

Graph matching with subdivision surfaces for texture synthesis on surfaces

Existing texture synthesis-from-example strategies for polygon meshes typically make use of three components: a multi-resolution mesh hierarchy that allows the overall nature of the pattern to be reproduced before filling in detail; a matching strategy that extends the synthesized texture using the best fit from a texture sample; and a transfer mechanism that copies the selected portion of the texture sample to the target surface. We introduce novel alternatives for each of these components. Use of p2-subdivision surfaces provides the mesh hierarchy and allows fine control over the surface complexity. Adaptive subdivision is used to create an even vertex distribution over the surface. Use of the graph defined by a surface region for matching, rather than a regular texture neighbourhood, provides for flexible control over the scale of the texture and allows simultaneous matching against multiple levels of an image pyramid created from the texture sample. We use graph cuts for texture transfer, adapting this scheme to the context of surface synthesis. The resulting surface textures are realistic, tolerant of local mesh detail and are comparable to results produced by texture neighbourhood sampling approaches.

Reliable graph discovery

A critical question in data mining is that can we always trust what discovered by a data mining system unconditionally? The answer is obviously not. If not, when can we trust the discovery then? What are the factors that affect the reliability of the discovery? How do they affect the reliability of the discovery? These are some interesting questions to be investigated. In this chapter we will firstly provide a definition and the measurements of reliability, and analyse the factors that affect the reliability. We then examine the impact of model complexity, weak links, varying sample sizes and the ability of different learners to the reliability of graphical model discovery. The experimental results reveal that (1) the larger sample size for the discovery, the higher reliability we will get; (2) the stronger a graph link is, the easier the discovery will be and thus the higher the reliability it can achieve; (3) the complexity of a graph also plays an important role in the discovery. The higher the complexity of a graph is, the more difficult to induce the graph and the lower reliability it would be. We also examined the performance difference of different discovery algorithms. This reveals the impact of discovery process. The experimental results show the superior reliability and robustness of MML method to standard significance tests in the recovery of graph links with small samples and weak links.

Discovering semantics from visualizations of film takes

In this paper, we study the application of a scene structure visualizing technique called Double-Ring Take-Transition-Diagram (DR-TTD). This technique presents takes and their transitions during a film scene via nodes and edges of a 'graph' consisting of two rings as its backbone. We describe how certain filmic elements such as montage, centre/cutaway, dialogue, temporal flow, zone change, dramatic progression, shot association, scene introduction, scene resolution, master shot and editing orchestration can be identified from a scene through the signature arrangements of nodes and edges in the DR-TTD.

VGraph: graph virtualization towards big data

Virtualization brought an immense commute in the modern technology especially in computer networks since last decade. The enormity of big data has led the massive graphs to be increased in size exponentially in recent years so that normal tools and algorithms are going weak to process it. Size diminution of the massive graphs is a big challenge in the current era and extraction of useful information from huge graphs is also problematic. In this paper, we presented a concept to design the virtual graph vGraph in the virtual plane above the original plane having original massive graph and proposed a novel cumulative similarity measure for vGraph. The use of vGraph is utile in lieu of massive graph in terms of space and time. Our proposed algorithm has two main parts. In the first part, virtual nodes are designed from the original nodes based on the calculation of cumulative similarity among them. In the second part, virtual edges are designed to link the virtual nodes based on the calculation of similarity measure among the original edges of the original massive graph. The algorithm is tested on synthetic and real-world datasets which shows the efficiency of our proposed algorithms.

Groups, graphs and mutually unbiased bases

A new characteristic free approach to constructing large sets of mutually unbiased bases in Hilbert space is developed. We associate with a seed set of bases a finite subgroup of which defines a strongly regular graph. Large sets of mutually unbiased bases are obtained as the cliques of the graph.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

Minimum spanning trees for valley and ridge characterization in digital elevation maps

Texture synthesis employs neighbourhood matching to generate appropriate new content. Terrain synthesis has the added constraint that new content must be geographically plausible. The profile recognition and polygon breaking algorithm (PPA) [Chang et al. 1998] provides a robust mechanism for characterizing terrain as systems of valley and ridge lines in digital elevation maps. We exploit this to create a terrain characterization metric that is robust, efficient to compute and is sensitive to terrain properties.

Terrain regions are characterized as a minimum spanning tree derived from a graph created from the sample points of the elevation map which are encoded as weights in the edges of the graph. This formulation allows us to provide a single consistent feature definition that is sensitive to the pattern of ridges and valleys in the terrain Alternative formulations of these weights provide richer characteristicmeasures and we provide examples of alternate definitions based on curvature and contour measures.

We show that the measure is robust, with a significant portion derived directly from information local to the terrain sample. Global terrain characteristics introduce the issue of over- and underconnected valley/ridge lines when working with sub-regions. This is addressed by providing two graph construction strategies, which respectively provide an upper bound on connectivity as a single spanning tree, and a lower bound as a forest of trees.

Efficient minimum spanning tree algorithms are adapted to the context of terrain data and are shown to provide substantially better performance than previous PPA implementations. In particular, these are able to characterize valley and ridge behaviour at every point even in large elevation maps, providing a measure sensitive to terrain features at all scales.

The resulting graph based formulation provides an efficient and elegant algorithm for characterizing terrain features. The measure can be calculated efficiently, is robust under changes of neighbourhood position, size and resolution and the hybrid measure is sensitive to terrain features both locally and globally.

The L(2,1)-labelling problem for cubic Cayley graphs on dihedral groups

A k-L(2,1)-labelling of a graph G is a mapping f:V(G)→{0,1,2,…,k} such that |f(u)−f(v)|≥2 if uv∈E(G) and f(u)≠f(v) if u,v are distance two apart. The smallest positive integer k such that G admits a k-L(2,1)-labelling is called the λ-number of G. In this paper we study this quantity for cubic Cayley graphs (other than the prism graphs) on dihedral groups, which are called brick product graphs or honeycomb toroidal graphs. We prove that the λ-number of such a graph is between 5 and 7, and moreover we give a characterisation of such graphs with λ-number 5.

Robust nonlinear distributed controller design for active and reactive power sharing in islanded microgrids

This paper presents a robust nonlinear distributed controller design for islanded operation of microgrids in order to maintain active and reactive power balance. In this paper, microgrids are considered as inverter-dominated networks integrated with renewable energy sources (RESs) and battery energy storage systems (BESSs), where solar photovoltaic generators act as RESs and plug-in hybrid electric vehicles as BESSs to supply power into the grid. The proposed controller is designed by using partial feedback linearization and the robustness of this control scheme is ensured by considering structured uncertainties within the RESs and BESSs. An approach for modeling the uncertainties through the satisfaction of matching conditions is also provided in this paper. The proposed distributed control scheme requires information from local and neighboring generators to communicate with each other and the communication among RESs, BESSs, and control centers is developed by using the concept of the graph theory. Finally, the performance of the proposed robust controller is demonstrated on a test microgrid and simulation results indicate the superiority of the proposed scheme under different operating conditions as compared to a linear-quadratic-regulator-based controller.

Efficient mining of top-k breaker emerging subgraph patterns from graph datasets

This paper introduces a new type of discriminative subgraph pattern called breaker emerging subgraph pattern by introducing three constraints and two new concepts: base and breaker. A breaker emerging sub-graph pattern consists of three subpatterns: a con-strained emerging subgraph pattern, a set of bases and a set of breakers. An efficient approach is pro-posed for the discovery of top-k breaker emerging sub-graph patterns from graph datasets. Experimental re-sults show that the approach is capable of efficiently discovering top-k breaker emerging subgraph patterns from given datasets, is more efficient than two previ-ous methods for mining discriminative subgraph pat-terns. The discovered top-k breaker emerging sub-graph patterns are more informative, more discrim-inative, more accurate and more compact than the minimal distinguishing subgraph patterns. The top-k breaker emerging patterns are more useful for sub-structure analysis, such as molecular fragment analy-sis. © 2009, Australian Computer Society, Inc.

On the definition of an kinematic hardening effect graph for sheet metal forming process simulations

The objective of this work is to develop a kinematic hardening effect graph (KHEG) which can be used to evaluate the effect of kinematic hardening on the model accuracy of numerical sheet metal forming simulations and this without the need of complex material characterisation. The virtual manufacturing process design and optimisation depends on the accuracy of the constitutive models used to represent material behaviour. Under reverse strain paths the Bauschinger effect phenomenon is modelled using kinematic hardening models. However, due to the complexity of the experimental testing required to characterise this phenomenon in this work the KHEG is presented as an indicator to evaluate the potential benefit of carrying out these tests. The tool is validated with the classic three point bending process and the U-channel width drawbead process. In the same way, the capability of the KHEG to identify effects in forming processes that do not include forming strain reversals is identified.

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.

Propagation model of smartphone worms based on semi-Markov process and social relationship graph

Smartphone applications are getting more and more popular and pervasive in our daily life, and are also attractive to malware writers due to their limited computing source and vulnerabilities. At the same time, we possess limited understanding of our opponents in cyberspace. In this paper, we investigate the propagation model of SMS/MMS-based worms through integrating semi-Markov process and social relationship graph. In our modeling, we use semi-Markov process to characterize state transition among mobile nodes, and hire social network theory, a missing element in many previous works, to enhance the proposed mobile malware propagation model. In order to evaluate the proposed models, we have developed a specific software, and collected a large scale real-world data for this purpose. The extensive experiments indicate that the proposed models and algorithms are effective and practical. © 2014 Elsevier Ltd. All rights reserved.