Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

2011 international joint conference on neural networks (ijcnn)

Supervised feature selection techniques in network intrusion detection: A critical review

Degree centralization is a simple and widely used index of degree distribution concentration in social networks. Conventionally, the centralization score is normalized by the maximum value attainable at the observed order; this results in a measure on the unit interval. Here, exact bounds are provided for degree centralization given density as well as order. It is shown that roughly half of the region of conceivable degree centralization scores is actually feasible, and that the geometry of the feasible region alters with graph size. Concentration of the distribution of graphs within the feasible region is shown, and a renormalized family of degree centralization indices which adjusts for both density and order is provided.

Exact Bounds for Degree Centralization

Complex networks are ubiquitous to several computer science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore is a robust, effective alternative in real-world applications. The methodology and proposed machine-learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.

Machine Learning in Network Centrality Measures: Tutorial and Outlook

Measures of complex network analysis, such as vertex centrality, have the potential to unveil existing network patterns and behaviors. They contribute to the understanding of networks and their components by analyzing their structural properties, which makes them useful in several computer science domains and applications. Unfortunately, there is a large number of distinct centrality measures and little is known about their common characteristics in practice. By means of an empirical analysis, we aim at a clear understanding of the main centrality measures available, unveiling their similarities and differences in a large number of distinct social networks. Our experiments show that the vertex centrality measures known as information, eigenvector, subgraph, walk betweenness and betweenness can distinguish vertices in all kinds of networks with a granularity performance at 95%, while other metrics achieved a considerably lower result. In addition, we demonstrate that several pairs of metrics evaluate the vertices in a very similar way, i.e. their correlation coefficient values are above 0.7. This was unexpected, considering that each metric presents a quite distinct theoretical and algorithmic foundation. Our work thus contributes towards the development of a methodology for principled network analysis and evaluation.

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An Analysis of Centrality Measures for Complex and Social Networks

Vertex centrality measures are important analysis elements in complex networks and systems. These metrics have high space and time complexity, which is a severe problem in applications that typically involve large networks. To apply such high complexity metrics in large networks we trained and tested off-the-shelf machine learning algorithms on several generated networks using five well-known complex network models. Our main hypothesis is that if one uses low complexity metrics as inputs to train the algorithms, one will achieve good approximations of high complexity measures. Our results show that the regression output of the machine learning algorithms applied in our experiments successfully approximate the real metric values and are a robust alternative in real world applications, in particular in complex and social network analysis.

Estimating complex networks centrality via neural networks and machine learning

The analysis and study of complex networks are crucial to a number of applications. Vertex centrality measures are an important analysis mechanism to uncover or rank important elements of a given network. However, these metrics have high space and time complexity, which is a severe problem in applications that typically involve large networks. We propose and study the use of neural learning algorithms in such a way that the use of these metrics became feasible in networks of any size. We trained and tested 12 off-the-shelf learning algorithms on several networks. Our results show that the regression output of the machine learning algorithms successfully approximate the real metric values and are a robust alternative in real world applications. We also identified that the model generated by the multilayer layer network trained with the Levenberg-Marquardt algorithm achieved the best performance, both in process time and solution quality, among all the methodologies tested for this task.

On approximating networks centrality measures via neural learning algorithms

Complex networks are ubiquitous to several Computer Science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However, these metrics have high computational costs and requirements that hinder their applications in large real-world networks. In this tutorial, we explain how the use of neural network learning algorithms can render the application of the metrics in complex networks of arbitrary size. Moreover, the tutorial describes how to identify the best configuration for neural network training and learning such for tasks, besides presenting an easy way to generate and acquire training data. We do so by means of a general methodology, using complex network models adaptable to any application. We show that a regression model generated by the neural network successfully approximates the metric values and therefore are a robust, effective alternative in real-world applications. The methodology and proposed machine learning model use only a fraction of time with respect to other approximation algorithms, which is crucial in complex network applications.

Felipe Grando

Papers

Machine Learning in Network Centrality Measures: Tutorial and Outlook

An Analysis of Centrality Measures for Complex and Social Networks

Estimating complex networks centrality via neural networks and machine learning

On approximating networks centrality measures via neural learning algorithms

Machine Learning in Network Centrality Measures: Tutorial and Outlook