Incremental Algorithms For Centrality Metric Calculations In Social Network Analysis
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Incremental Algorithms for Centrality Metric Calculations in Social Network Analysis
In social network analysis, graph-theoretic concepts are used to understand andexplain social phenomena. Calculations of centrality metrics such as Degree central-ity, Closeness centrality, and Betweenness centrality are essential parts of networkanalysis. These centrality metrics indicate the prominence of individual nodes withina social network. In case of static graphs, algorithms have been designed and imple-mented to compute the aforementioned metrics. However if a graph is dynamic, thatis if some nodes and edges are added or deleted from a graph at some later stage, then recalculating values of these centrality metrics from scratch is a costly operationin terms of computational efforts and time. This work involves design and imple-mentation of incremental algorithms which will compute values of Degree, Closenessand Betweenness Centralities for dynamically changing social networks. Closenessand Betweenness Centralities are based on the concept of All Pairs Shortest Paths. Hence, the design of incremental algorithms for these Centrality Metrics are built onthe foundations of an incremental All Pairs Shortest Path algorithm. Our incrementalAll Pairs Shortest Path algorithm runs in O (n^2) time, which is an improvement overcurrent fastest implemented algorithm running in O (n^2 logn) time. We also show thatO (n^2) is a theoretical lower bound for Dynamic All Pairs Shortest Path algorithm. This work also compares repeated use of static algorithms for Centrality Metricswith the newly designed incremental algorithms. Results indicate that the incremen-tal versions are capable of running approximately 655 times faster than their staticcounterparts for a graph of 1000 nodes.
Frontier Computing
This book gathers the proceedings of the 12th International Conference on Frontier Computing, held in Tokyo, Japan, on July 12–15, 2022, and provides comprehensive coverage of the latest advances and trends in information technology, science, and engineering. It addresses a number of broad themes, including communication networks, business intelligence and knowledge management, Web intelligence, and related fields that inspire the development of information technology. The respective contributions cover a wide range of topics: database and data mining, networking and communications, Web and Internet of things, embedded systems, soft computing, social network analysis, security and privacy, optical communication, and ubiquitous/pervasive computing. Many of the papers outline promising future research directions, and the book benefits students, researchers, and professionals alike. Further, it offers a useful reference guide for newcomers to the field.
Responsible Graph Neural Networks
More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications. Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details. Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.