Graphs in machine learning: an introduction

Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences. In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised methods. Unsupervised learning algorithms usually aim at visualising graphs in latent spaces and/or clustering the nodes. Both focus on extracting knowledge from graph topologies. While most existing techniques are only applicable to static graphs, where edges do not evolve through time, recent developments have shown that they could be extended to deal with evolving networks. In a supervised context, one generally aims at inferring labels or numerical values attached to nodes using both the graph and, when they are available, node characteristics. Balancing the two sources of information can be challenging, especially as they can disagree locally or globally. In both contexts, supervised and un-supervised, data can be relational (augmented with one or several global graphs) as described above, or graph valued. In this latter case, each object of interest is given as a full graph (possibly completed by other characteristics). In this context, natural tasks include graph clustering (as in producing clusters of graphs rather than clusters of nodes in a single graph), graph classification, etc. 1 Real networks One of the first practical studies on graphs can be dated back to the original work of Moreno [51] in the 30s. Since then, there has been a growing interest in graph analysis associated with strong developments in the modelling and the processing of these data. Graphs are now used in many scientific fields. In Biology [54, 2, 7], for instance, metabolic networks can describe pathways of biochemical reactions [41], while in social sciences networks are used to represent relation ties between actors [66, 56, 36, 34]. Other examples include powergrids [71] and the web [75]. Recently, networks have also been considered in other areas such as geography [22] and history [59, 39]. In machine learning, networks are seen as powerful tools to model problems in order to extract information from data and for prediction purposes. This is the object of this paper. For more complete surveys, we refer to [28, 62, 49, 45]. In this section, we introduce notations and highlight properties shared by most real networks. In Section 2, we then consider methods aiming at extracting information from a unique network. We will particularly focus on clustering methods where the goal is to find clusters of vertices. Finally, in Section 3, techniques that take a series of networks into account, where each network i

Bidding Strategy with Forecast Technology Based on Support Vector Machine in Electrcity Market

The participants of the electricity market concern very much the market price evolution. Various technologies have been developed for price forecast. SVM (Support Vector Machine) has shown its good performance in market price forecast. Two approaches for forming the market bidding strategies based on SVM are proposed. One is based on the price forecast accuracy, with which the being rejected risk is defined. The other takes into account the impact of the producer's own bid. The risks associated with the bidding are controlled by the parameters setting. The proposed approaches have been tested on a numerical example.Comment: 8pages, 13figures, paper for the conference "Applications of Physics in Financial Analysis 6th International Conference

Evolutionary Algorithms for Community Detection in Continental-Scale High-Voltage Transmission Grids

Symmetry is a key concept in the study of power systems, not only because the admittance and Jacobian matrices used in power flow analysis are symmetrical, but because some previous studies have shown that in some real-world power grids there are complex symmetries. In order to investigate the topological characteristics of power grids, this paper proposes the use of evolutionary algorithms for community detection using modularity density measures on networks representing supergrids in order to discover densely connected structures. Two evolutionary approaches (generational genetic algorithm, GGA+, and modularity and improved genetic algorithm, MIGA) were applied. The results obtained in two large networks representing supergrids (European grid and North American grid) provide insights on both the structure of the supergrid and the topological differences between different regions. Numerical and graphical results show how these evolutionary approaches clearly outperform to the well-known Louvain modularity method. In particular, the average value of modularity obtained by GGA+ in the European grid was 0.815, while an average of 0.827 was reached in the North American grid. These results outperform those obtained by MIGA and Louvain methods (0.801 and 0.766 in the European grid and 0.813 and 0.798 in the North American grid, respectively)

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Application of Neural-Like P Systems With State Values for Power Coordination of Photovoltaic/Battery Microgrids

The power coordination control of a photovoltaic/battery microgrid is performed with a novel bio-computing model within the framework of membrane computing. First, a neural-like P system with state values (SVNPS) is proposed for describing complex logical relationships between different modes of Photovoltaic (PV) units and energy storage units. After comparing the objects in the neurons with the thresholds, state values will be obtained to determine the con guration of the SVNPS. Considering the characteristics of PV/battery microgrids, an operation control strategy based on bus voltages of the point of common coupling and charging/discharging statuses of batteries is proposed. At rst, the SVNPS is used to construct the complicated unit working modes; each unit of the microgrid can adjust the operation modes automatically. After that, the output power of each unit is reasonably coordinated to ensure the operation stability of the microgrid. Finally, a PV/battery microgrid, including two PV units, one storage unit, and some loads are taken into consideration, and experimental results show the feasibility and effectiveness of the proposed control strategy and the SVNPS-based power coordination control models

A virtual actuator approach for the secure control of networked LPV systems under pulse-width modulated DoS attacks

In this paper, we formulate and analyze the problem of secure control in the context of networked linear parameter varying (LPV) systems. We consider an energy-constrained, pulse-width modulated (PWM) jammer, which corrupts the control communication channel by performing a denial-of-service (DoS) attack. In particular, the malicious attacker is able to erase the data sent to one or more actuators. In order to achieve secure control, we propose a virtual actuator technique under the assumption that the behavior of the attacker has been identified. The main advantage brought by this technique is that the existing components in the control system can be maintained without need of retuning them, since the virtual actuator will perform a reconfiguration of the plant, hiding the attack from the controller point of view. Using Lyapunov-based results that take into account the possible behavior of the attacker, design conditions for calculating the virtual actuators gains are obtained. A numerical example is used to illustrate the proposed secure control strategy.Peer ReviewedPostprint (author's final draft