Link Prediction in Complex Network via Penalizing Noncontribution Relations of Endpoints

Similarity based link prediction algorithms become the focus in complex network research. Although endpoint degree as source of influence diffusion plays an important role in link prediction, some noncontribution links, also called noncontribution relations, involved in the endpoint degree serve nothing to the similarity between the two nonadjacent endpoints. In this paper, we propose a novel link prediction algorithm to penalize those endpoints’ degrees including many null links in influence diffusion, namely, noncontribution relations penalization algorithm, briefly called NRP. Seven mainstream baselines are introduced for comparison on nine benchmark datasets, and numerical analysis shows great improvement of accuracy performance, measured by the Area Under roc Curve (AUC). At last, we simply discuss the complexity of our algorithm

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.Comment: Final version published in Physics Reports. More information available at http://synchronets.googlepages.com

To what extent homophily and influencer networks explain song popularity

Forecasting the popularity of new songs has become a standard practice in the music industry and provides a comparative advantage for those that do it well. Considerable efforts were put into machine learning prediction models for that purpose. It is known that in these models, relevant predictive parameters include intrinsic lyrical and acoustic characteristics, extrinsic factors (e.g., publisher influence and support), and the previous popularity of the artists. Much less attention was given to the social components of the spreading of song popularity. Recently, evidence for musical homophily - the tendency that people who are socially linked also share musical tastes - was reported. Here we determine how musical homophily can be used to predict song popularity. The study is based on an extensive dataset from the last.fm online music platform from which we can extract social links between listeners and their listening patterns. To quantify the importance of networks in the spreading of songs that eventually determines their popularity, we use musical homophily to design a predictive influence parameter and show that its inclusion in state-of-the-art machine learning models enhances predictions of song popularity. The influence parameter improves the prediction precision (TP/(TP+FN)) by about 50% from 0.14 to 0.21, indicating that the social component in the spreading of music plays at least as significant a role as the artist's popularity or the impact of the genre.Comment: 7 pages, 3 figure

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

COMMUNITY DETECTION AND INFLUENCE MAXIMIZATION IN ONLINE SOCIAL NETWORKS

The detecting and clustering of data and users into communities on the social web are important and complex issues in order to develop smart marketing models in changing and evolving social ecosystems. These marketing models are created by individual decision to purchase a product and are influenced by friends and acquaintances. This leads to novel marketing models, which view users as members of online social network communities, rather than the traditional view of marketing to individuals. This thesis starts by examining models that detect communities in online social networks. Then an enhanced approach to detect community which clusters similar nodes together is suggested. Social relationships play an important role in determining user behavior. For example, a user might purchase a product that his/her friend recently bought. Such a phenomenon is called social influence and is used to study how far the action of one user can affect the behaviors of others. Then an original metric used to compute the influential power of social network users based on logs of common actions in order to infer a probabilistic influence propagation model. Finally, a combined community detection algorithm and suggested influence propagation approach reveals a new influence maximization model by identifying and using the most influential users within their communities. In doing so, we employed a fuzzy logic based technique to determine the key users who drive this influence in their communities and diffuse a certain behavior. This original approach contrasts with previous influence propagation models, which did not use similarity opportunities among members of communities to maximize influence propagation. The performance results show that the model activates a higher number of overall nodes in contemporary social networks, starting from a smaller set of key users, as compared to existing landmark approaches which influence fewer nodes, yet employ a larger set of key users

Online Social Networks: Measurements, Analysis and Solutions for Mining Challenges

In the last decade, online social networks showed enormous growth. With the rise of these networks and the consequent availability of wealth social network data, Social Network Analysis (SNA) led researchers to get the opportunity to access, analyse and mine the social behaviour of millions of people, explore the way they communicate and exchange information. Despite the growing interest in analysing social networks, there are some challenges and implications accompanying the analysis and mining of these networks. For example, dealing with large-scale and evolving networks is not yet an easy task and still requires a new mining solution. In addition, finding communities within these networks is a challenging task and could open opportunities to see how people behave in groups on a large scale. Also, the challenge of validating and optimizing communities without knowing in advance the structure of the network due to the lack of ground truth is yet another challenging barrier for validating the meaningfulness of the resulting communities. In this thesis, we started by providing an overview of the necessary background and key concepts required in the area of social networks analysis. Our main focus is to provide solutions to tackle the key challenges in this area. For doing so, first, we introduce a predictive technique to help in the prediction of the execution time of the analysis tasks for evolving networks through employing predictive modeling techniques to the problem of evolving and large-scale networks. Second, we study the performance of existing community detection approaches to derive high quality community structure using a real email network through analysing the exchange of emails and exploring community dynamics. The aim is to study the community behavioral patterns and evaluate their quality within an actual network. Finally, we propose an ensemble technique for deriving communities using a rich internal enterprise real network in IBM that reflects real collaborations and communications between employees. The technique aims to improve the community detection process through the fusion of different algorithms

Adaptive dynamical networks

It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks