Principles of Big Graph: In-Depth Insight: Volume 128

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.

《大圖原理：深入洞察，第128卷》是《計算機進展》系列中的一本新書，該卷以多個有趣的章節介紹了該領域的新進展。這些章節包括：CESDAM：用於大圖表示的中心子圖數據矩陣、NoSQL解決方案在大圖應用中的雙變量、聚類和適應性分析、使用深度學習對大圖進行實證研究、分析圖聚類的質量和準確性之間的相關性、使用布隆過濾器對蛋白編碼基因圖數據進行過濾、使用替代表示處理大圖、基於MapReduce的卷積圖神經網絡：全面回顧。

使用SIMD加速在大圖中快速準確地計算三角形數量、對攻擊圖進行全面調查、二進制樹的量子位表示及其在量子計算中的操作、在大型社交網絡圖數據上的多標籤文本分類中修改的ML-KNN：相似度度量和最近鄰配置的作用、基於大圖的在線學習通過社交網絡、大規模實際網絡中的社區檢測、Power rank：一種交互式網頁排名算法、基於遺傳算法的無線傳感器網絡的能源效率建模、大圖的主要挑戰及其解決方案：一項綜述，以及對社會網絡犯罪圖的調查。