Principles of big graph : in-depth insight
Author(s)
Bibliographic Information
Principles of big graph : in-depth insight
(Advances in computers, v. 128)
Academic Press, an imprint of Elsevier, c2023
Available at 2 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
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
by "Nielsen BookData"