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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (7): 1-11    DOI: 10.11925/infotech.2096-3467.2021.1168
Original article Current Issue | Archive | Adv Search |
Graph Databases for Complex Network Analysis
Liu Chunjiang1,2(),Li Shuying1,Hu Hanlin3,Fang Shu1,2
1Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
3School of Public Administration, Sichuan School of Economics and Management, University, Chengdu 610065, China
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Abstract  

[Objective] This paper systematically reviews the progress and trends of graph database research and applications for complex network analysis. [Coverage] We searched the Web of Science, Scopus, and CNKI database for Chinese and English literature. A total of 15 graph databases and open-source packages, 21 practical cases, and 14 research papers were retrieved. [Methods] First, we compared the mainstream graph database products from China and abroad. Then, we explored the latest solutions for complex network analysis, including algorithms (such as centrality, path finding, link prediction, and community detection), graph visualization, performance and related applications. [Results] The graph database has become an important analysis tool and research method for complex network analysis and big data mining. They also work closely with graph computing engines for complex network analysis. [Limitations] This paper only examined a few representative cases. [Conclusions] The graph database could effectively query, represent and analyze complex network data for their patterns or structures. Their presentation of multi-dimensional data is crucial for mining implicit relationships.
Key words： Knowledge Graph      Graph Database      Complex Network     
Received: 14 October 2021      Published: 31 December 2021
ZTFLH:  TP391  
Fund:National Social Science Fund of China(19BTQ088);2020 Western Light Talent Project of the Chinese Academy of Sciences(E1C0000201)
Corresponding Authors: Liu Chunjiang,ORCID:0000-0001-8934-339X     E-mail: liucj@clas.ac.cn
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Chunjiang Liu
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Cite this article:

Liu Chunjiang, Li Shuying, Hu Hanlin, Fang Shu. Graph Databases for Complex Network Analysis. Data Analysis and Knowledge Discovery, 2022, 6(7): 1-11.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1168     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I7/1

Popularity Changes per Category by DB-Engines
对比项目 简单图 多关系 多模 图数据库
节点类型 1种 1种 1种或更多 1种或更多
边类型 1种 1种或更多 1种 1种或更多
原始来源信息 外部 外部 外部 内部
支持的最大节点数量（个） <106 <106 <106 >109
代表软件 NetworkX, Iggraph Ucinet, Statnet, PNet Neo4j, Titan, DSE Graph
Graph Database vs. Multi-Relationship vs. Multi-Modal[9]
名称 查询语言 支持接口 集成算法
GDB[10] Gremlin & Cypher HTTP REST, Java, Python,.NET,
GO, Node.js		 支持PageRank、中介中心性、循环检测、Recommendation、最短路径等多种算法
HugeGraph[11] Gremlin RESTful API, Java Client, Gremlin
Console		 支持路径搜索、协同推荐、联通子图、PersonalRank、相似度、中心性等多种算法
BGraph[12] Gremlin HTTP API, SDK 支持特色的高性能图算法
TGDB[13] Cypher 暂无数据 支持PageRank、社群发现、相似度计算、模糊子图匹配等社群发现算法
GeaBase[14] 自研查询语言 GeaBase 客户端（Java） 支持社区、推荐等算法
Nebula Graph[15] nGQL C++, GO, Python, Java 支持PageRank、社区检测、最短路径、介数中心性等多种算法
TuGraph[16] OpenCypher HTTP API 支持PageRank、路径、连通、中心性、社区等多种算法
Domestic Mainstream Graph Database
名称 查询语言 支持接口 集成算法
Neo4j[17] Cypher Java, Python, JavaScript,.NET, GO, REST, HTTP API 支持Centrality、Community Detection、Similarity、
Path Finding、Link Prediction等多类算法
TigerGraph[18] GSQL REST API, Remote GSQL Client（Running by Java） 支持Path、Centrality、Community、Similarity、
Classification等5类算法
Neptune[19] Gremlin & SPARQL HTTP REST, Java, Python,.NET, Node.js 无
JanusGraph[20] Gremlin Java, Python,.NET 支持PageRank等算法
ArangoDB[21] AQL HTTP API, Java, JavaScript, PHP, GO,
Python, C#, Scala, C++, R等		 支持路径搜索算法
AllegroGraph[22] SPARQL & Prolog Java、Python、Lisp、HTTP 支持多种中心性算法
OrientDB[23] SQL Java, Python, PHP, C++, JavaScript等 支持SB-Tree、Hash、Lucene Full Text、Lucene
Spatial等4种索引算法
Foreign Mainstream Graph Database
应用场景 类型 图算法/图布局 具体描述
中心性分析[25] 基于相邻节点 PageRank、ArticleRank、Eigenvector Centrality等 基于相邻节点的重要性计算当前节点的重要性
基于节点自身 度中心性（Degree Centrality） 基于与相邻节点直接相连的关系数量计算当前节点的重要性
接近中心性（Closeness Centrality） 基于与其他节点之间的平均距离计算当前节点的重要性
中介中心性（Between Centrality） 基于当前节点出现在任意两个节点的最短路径中的次数计算当前节点的重要性
路径查找[26] 路径遍历 广度优先搜索（Breadth First Search） 通过逐层遍历所有相邻节点从而找到最短路径
深度优先搜索（Depth First Search） 对起始节点的所有分支逐个遍历
固定路径查找 最短路径（Dijkstra、A*、Yen’s） 计算两个节点之间的最短路径
所有节点对最短路径（All Pairs Shortest Path） 计算所有节点之间的最短路径
单源最短路径（Single Source Shortest Path） 计算源节点与其他可达节点之间的最短路径
最小权重生成树（Minimum Weight Spanning Tree） 计算生成树中边权值和最小情况下的路径
非固定路径查找 随机游走（Random Walk） 在指定路径长度内,计算从一个节点开始按照随机或非随机的方式选择下一个节点的路径
链接预测[26] 基于邻居节点 所有邻居（Total Neighbors） 基于两个节点的邻居节点集合并集,计算两个节点的紧密度
连接偏好（Preferential Attachment） 对节点连接数有偏好,因此将两个节点的邻居节点集合数量相乘,计算两个节点的紧密度
基于共有邻居 共有邻居（Common Neighbors） 基于两个节点的邻居节点集合交集,计算两个节点的紧密度
资源优化（Resource Allocation） 基于共有邻居的相邻节点集合,并对集合数量进行非线性归一化处理,计算两个节点的紧密度
AA（Adamic Adar） 基于共有邻居的相邻节点集合,未对集合数量进行非线性归一化处理,计算两个节点的紧密度
基于共有社区 共有社区（Same Community） 基于社区检测算法判断两个节点是否属于相同社区,计算两个节点的紧密度
社区检测[27] 基于模块度 鲁汶（Louvain） 基于社区的模块度进行社区划分
基于网络动力学 标签传播（Label Propagation） 基于节点标签的传播进行社区划分
基于簇 弱联通社区（Weakly Connected Components） 在无向图中将任意节点间均存在路径的集合形成簇,计算出社区中所有的簇
强联通簇（Strongly Connected Components） 在有向图中将任意节点间均存在双向路径的集合形成簇,计算出社区中所有的簇
基于三角形 三角计数（Triangle Count） 计算社区中相连成三角形的节点集合
局部聚类系数（Local Clustering Coefficient） 基于节点的三角形和度这两个数值,计算节点的聚类系数
图可视化[28] 集成可视化 图布局类型比较简单,以力导向布局为主 通过图数据库集成的内置可视化工具
在线可视化 图布局类型最为丰富,包括力导向、地图、圆形、
时序、树状等布局		 通过Web前端的JavaScript可视化工具
Main Graph Algorithms/Graph Layouts in Complex Network Analysis
应用场景 主要算法 图数据库
预测重大疾病保险
欺诈[29]		 度中心性、中介中心性、接近中心性、特征向量中心性 Neo4j
面向科技与能力的
网络分析[30]		 度中心性、紧密中心性、中介中心性、特征向量中心性 HugeGraph
分析社交网络中心
节点[31]		 中心性、PageRank OrientDB
分析药品处方模式[32] 度中心性、中介中心性 Neo4j
侦查银行欺诈问题[33] 中心性、PageRank TigerGraph
Application of Graph Database in Centrality Analysis
应用场景 主要算法 图数据库
分析城市转供电方案[34] 广度优先搜索、深度优先搜索 Neo4j
解决QoS感知的Web
服务组合问题[35]		 Dijkstra算法 Neo4j
分析国家交通网络[36] Yen的K条最短路径算法（KSP） Neo4j
分析生物医学网络[37] 最短路径算法 OrientDB
Application of Graph Database in Path Finding
应用场景 主要算法 图数据库
结合网络结构相似度预测社会关系[38] Common Neighbors Neo4j
基于链路预测的生物医学知识发现[39] Adamic-Adar Neo4j
结合深度强化学习构建链路预测模型[40] Common Neighbors、Adamic-Adar OrientDB
基于链路预测的作者消歧[41] Common Neighbors、Adamic-Adar Neo4j
Application of Graph Database in Link Prediction
应用场景 主要算法 图数据库
基于社区检测算法的在线金融欺诈检测[42] Louvain、BMLPA Neo4j
基于改进关系权重的科研社区挖掘[43] Louvain Neo4j
基于社区检测算法的社交网络分析[44] Louvain、Edge Betweeness、Walktrap、CNM Neo4j
基于社区检测算法的道路交通网络分析[45] Louvain Neo4j
Application of Graph Database to Community Detection
应用场景 应用方式 图可视化工具 图数据库
知识关联可视化[47] 在线可视化 Echarts.js Neo4j
面向医学知识图谱的可视化[48] 集成可视化 Neo4j Neo4j
生物医学数据检索及可视化[49] 在线可视化 Cytoscape.js Neo4j
Twitter Troll 数据集可视化[50] 集成可视化 Neo4j Neo4j
Application of Graph Database in Graph Visualization
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