Principles of Big Graph: In-depth Insight

Principles of Big Graph: In-depth Insight, Volume 129 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation,  MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. Provides an update on the issues and challenges faced by current researchers Updates on future research agendas Includes advanced topics for intensive research for researchers INDICE: Preface Ripon Patgiri, Ganesh ChandraDeka and Anupam Biswas1. CESDAM: Centered subgraph data matrix for large graph representation Anupam Biswas and Bhaskar Biswas 2. Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications Samiya Khan, Xiufeng Liu, Syed Arshad Ali and Mansaf Alam3. An empirical investigation on BigGraph using deep learning Lilapati Waikhom and Ripon Patgiri4. Analyzing correlation between quality and accuracy of graph clustering Soumita Das and Anupam Biswas5. geneBF: Filtering protein-coded gene graph data using bloom filter Sabuzima Nayak and Ripon Patgiri 6. Processing large graphs with an alternative representation Ravi Kishore Devarapalli and Anupam Biswas7. MapReduce based convolutional graph neural networks: A comprehensive review U. Kartheek Chandra Patnaik and Ripon Patgiri8. Fast exact triangle counting in large graphs using SIMD acceleration Kaushik Ravichandran, Akshara Subramaniasivam, Aishwarya PS and Kumar NS9. A comprehensive investigation on attack graphs M Franckie Singha and Ripon Patgiri10. Qubit representation of a binary tree and its operations in quantum computation Arnab Roy, Joseph L Pachuau and Anish Kumar Saha11. Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph dataSaurabh Kumar Srivastava, Ankit Vidyarthi and Sandeep Kumar Singh12. Big graph based online learning through social networks Rahul Chandra Kushwaha13. Community detection in large-scale real-world networks Dhananjay Kumar Singh and Prasenjit Choudhury14. Power rank: An interactive web page ranking algorithm Ankit Vidyarthi and Pawan Singh15. GA based energy efficient modelling of a wireless sensor network Anish Kumar Saha, Joseph L Pachuau, Arnab Roy and C. T. Bhunia16. The major challenges of big graph and their solutions: A review Fitsum Gebreegziabher and Ripon Patgiri17. An investigation on socio-cyber crime graph V S NageswaraRao Kadiyala and Ripon Patgiri

• ISBN: 978-0-323-89810-2
• Editorial: Academic Press
• Encuadernacion: Cartoné
• Páginas: 310
• Fecha Publicación: 01/02/2023
• Nº Volúmenes: 1
• Idioma: Inglés