Principles of Big Graph: In-depth Insight

Preface
Ripon Patgiri, Ganesh ChandraDeka and Anupam Biswas
1. 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 Alam
3. An empirical investigation on BigGraph using deep learning
Lilapati Waikhom and Ripon Patgiri
4. Analyzing correlation between quality and accuracy of graph clustering
Soumita Das and Anupam Biswas
5. 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 Biswas
7. MapReduce based convolutional graph neural networks: A comprehensive review
U. Kartheek Chandra Patnaik and Ripon Patgiri
8. Fast exact triangle counting in large graphs using SIMD acceleration
Kaushik Ravichandran, Akshara Subramaniasivam, Aishwarya PS and Kumar NS
9. A comprehensive investigation on attack graphs
M Franckie Singha and Ripon Patgiri
10. Qubit representation of a binary tree and its operations in quantum computation
Arnab Roy, Joseph L Pachuau and Anish Kumar Saha
11. Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data
Saurabh Kumar Srivastava, Ankit Vidyarthi and Sandeep Kumar Singh
12. Big graph based online learning through social networks
Rahul Chandra Kushwaha
13. Community detection in large-scale real-world networks
Dhananjay Kumar Singh and Prasenjit Choudhury
14. Power rank: An interactive web page ranking algorithm
Ankit Vidyarthi and Pawan Singh
15. GA based energy efficient modelling of a wireless sensor network
Anish Kumar Saha, Joseph L Pachuau, Arnab Roy and C. T. Bhunia
16. The major challenges of big graph and their solutions: A review
Fitsum Gebreegziabher and Ripon Patgiri
17. An investigation on socio-cyber crime graph
V S NageswaraRao Kadiyala and Ripon Patgiri

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• Engenharia do software
• Matemática

Adjacency matrix; Attack graph; Big data; Big data system; Big graph; Big graph data; Big graphs; Big networks; BigGraph; Bioinformatics; Bivariate analysis; Bloom filter; Breadth-first search; CESDAM; Classification; Cluster analysis; Collaborating learning; Community detection; Community structures; Complex networks; Correlation analysis; Criminals; Crossover; Cyber attacks; Cyber stalking; Deep learning; Digital world; Dynamic algorithms; Dynamic communities; Ego network; E-mail bombing; E-mail spoofing; Energy optimization; Exact triangle counting; Financial crimes; Genetic algorithm; Graph analysis; Graph clustering algorithms; Graph convolutional network; Graph data; Graph processing; Graph representation; Graphs; Information system; IoT; Large graph representation; Large-scale graph; Large-scale graphs; Local community; Machine learning; MapReduce; Medical data; Meta-graph; Multi-label classification; Mutation; Node; NoSQL; Online learning; Overlapping communities; Page ranking; Protein-coding gene; Quality and accuracy measures; Quantum binary tree; Quantum gate; Qubit; Set operations; SIMD; SNAP; Social learning networks; Social network analysis; Social networks; Storage solution; Superposition; Telecommunication fraud; Textbook enrichment; Tree; Tree operations; Vector instructions; Vulnerabilities; Web crawling; Web system development; WSN