Principles of Big Graph: In-depth Insight, Volume 128 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.
1. Cover image - p. i 2. Table of Contents - p. iii 3. Copyright - p. iii 4. Contributors - p. iii 5. Preface - p. iii 6. Chapter One: CESDAM: Centered subgraph data matrix for large graph representation - p. 1 7. Chapter Two: Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications - p. 61 8. Chapter Three: An empirical investigation on BigGraph using deep learning - p. 126 9. Chapter Four: Analyzing correlation between quality and accuracy of graph clustering - p. 135 10. Chapter Five: geneBF: Filtering protein-coded gene graph data using Bloom filter - p. 165 11. Chapter Six: Processing large graphs with an alternative representation - p. 198 12. Chapter Seven: MapReduce based convolutional graph neural networks: A comprehensive review - p. 213 13. Chapter Eight: Fast exact triangle counting in large graphs using SIMD acceleration - p. 242 14. Chapter Nine: A comprehensive investigation on attack graphs - p. 251 15. Chapter Ten: Qubit representation of a binary tree and its operations in quantum computation - p. 273 16. Chapter Eleven: Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi-label text classification on big social network graph data - p. 287 17. Chapter Twelve: Big graph based online learning through social networks - p. 313 18. Chapter Thirteen: Community detection in large-scale real-world networks - p. 329 19. Chapter Fourteen: Power rank: An interactive web page ranking algorithm - p. 353 20. Chapter Fifteen: GA-based energy efficient modeling of a wireless sensor network - p. 381 21. Chapter Sixteen: The major challenges of big graph and their solutions: A review - p. 415 22. Chapter Seventeen: An investigation on socio-cyber crime graph - p. 423
Principles of Big Graph: In-depth Insight, Volume 128 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.
1. Cover image - p. i 2. Table of Contents - p. iii 3. Copyright - p. iii 4. Contributors - p. iii 5. Preface - p. iii 6. Chapter One: CESDAM: Centered subgraph data matrix for large graph representation - p. 1 7. Chapter Two: Bivariate, cluster, and suitability analysis of NoSQL solutions for big graph applications - p. 61 8. Chapter Three: An empirical investigation on BigGraph using deep learning - p. 126 9. Chapter Four: Analyzing correlation between quality and accuracy of graph clustering - p. 135 10. Chapter Five: geneBF: Filtering protein-coded gene graph data using Bloom filter - p. 165 11. Chapter Six: Processing large graphs with an alternative representation - p. 198 12. Chapter Seven: MapReduce based convolutional graph neural networks: A comprehensive review - p. 213 13. Chapter Eight: Fast exact triangle counting in large graphs using SIMD acceleration - p. 242 14. Chapter Nine: A comprehensive investigation on attack graphs - p. 251 15. Chapter Ten: Qubit representation of a binary tree and its operations in quantum computation - p. 273 16. Chapter Eleven: Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi-label text classification on big social network graph data - p. 287 17. Chapter Twelve: Big graph based online learning through social networks - p. 313 18. Chapter Thirteen: Community detection in large-scale real-world networks - p. 329 19. Chapter Fourteen: Power rank: An interactive web page ranking algorithm - p. 353 20. Chapter Fifteen: GA-based energy efficient modeling of a wireless sensor network - p. 381 21. Chapter Sixteen: The major challenges of big graph and their solutions: A review - p. 415 22. Chapter Seventeen: An investigation on socio-cyber crime graph - p. 423
