This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.

Link prediction is required to understand the evolutionary theory of computing for different social networks. However, the stochastic growth of the social network leads to various challenges in identifying hidden links, such as representation of graph, distinction between spurious and missing links, selection of link prediction techniques comprised of network features, and identification of network types. Hidden Link Prediction in Stochastic Social Networks concentrates on the foremost techniques of hidden link predictions in stochastic social networks including methods and approaches that involve similarity index techniques, matrix factorization, reinforcement, models, and graph representations and community detections. The book also includes miscellaneous methods of different modalities in deep learning, agent-driven AI techniques, and automata-driven systems and will improve the understanding and development of automated machine learning systems for supervised, unsupervised, and recommendation-driven learning systems. It is intended for use by data scientists, technology developers, professionals, students, and researchers.

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Social network analysis has created novel opportunities within the field of data science. The complexity of these networks requires new techniques to optimize the extraction of useful information. Graph Theoretic Approaches for Analyzing Large-Scale Social Networks is a pivotal reference source for the latest academic research on emerging algorithms and methods for the analysis of social networks. Highlighting a range of pertinent topics such as influence maximization, probabilistic exploration, and distributed memory, this book is ideally designed for academics, graduate students, professionals, and practitioners actively involved in the field of data science.

This book presents new and innovative current discoveries in social networking which contribute enough knowledge to the research community. The book includes chapters presenting research advances in social network analysis and issues emerged with diverse social media data. The book also presents applications of the theoretical algorithms and network models to analyze real-world large-scale social networks and the data emanating from them as well as characterize the topology and behavior of these networks. Furthermore, the book covers extremely debated topics, surveys, future trends, issues, and challenges.

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

Social Networks (SNs) have attracted many users and have become an integrated part of the individual’s daily practices. The rapid climb of SNs like Twitter and Facebook has generated a great deal of knowledge that sets direction for research in social relationships. The knowledge network represented by Facebook is predicated on information transmission, sharing, and exchange. The prediction process from prior information of the event helps to know the evolution of social networks and assists the companies in effective decision making during a typical recommendation system . Social network connection prediction is an efficient technique for the analysis of the evolution of social organizations and formation of the social network relations.Link prediction is a crucial research direction within the field of complex networks and data processing . Some complex physical processes like local stochastic processes also are wont to measure the similarity between network nodes and improve the accuracy of the link prediction . In other words two linked nodes during a network may have a possible relationship. Analyzing whether there's a possible relationship can help to seek out potential links and tightness measures the intensity of the connection. Currently with the rapid development, online social networks have been a neighborhood of people’s life.