图算法实战：Apache Spark与Neo4j平台的应用

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"《Neo4j_Graph_Algorithms_r3.pdf》是一本由Mark Needham和Amy E. Hodler合著的实践指南，专为熟悉Apache Spark或Neo4j的开发者和数据科学家设计，旨在介绍如何在Neo4j中应用各种图算法来解决知识图谱中的问题。书中的算法示例利用了Spark和Neo4j这两个平台。" 本书详细阐述了图算法在实际项目中的应用，特别关注于Neo4j这个强大的图形数据库系统。Neo4j是一种面向关系的数据模型，非常适合处理复杂网络结构，如知识图谱。知识图谱是由实体（如人、地点、事件等）及其相互关系构成的网络，通过图算法，我们可以高效地查询、分析和理解这些复杂的数据关系。书中可能涵盖了以下关键知识点： 1. **图数据模型**：解释了图数据模型的基础，包括节点、边（关系）和属性，以及它们如何在Neo4j中表示和操作。 2. **图算法基础**：介绍了基本的图算法概念，如最短路径算法（Dijkstra、Bellman-Ford）、遍历算法（深度优先搜索DFS、广度优先搜索BFS）以及图的连通性检查。 3. **社区检测**：讲解了如何使用图算法识别图中的社区结构，如Louvain方法和 modularity优化。 4. **聚类与相似性**：讨论了节点聚类算法（例如，Watts-Strogatz小世界模型和Barabási-Albert无标度网络）以及计算节点相似性的方法，如Jaccard相似度和PageRank算法。 5. **图遍历策略**：详述了如何利用Traversal Framework和Cypher查询语言在Neo4j中有效地执行图遍历。 6. **Apache Spark集成**：展示了如何将Spark与Neo4j结合，以处理大规模图数据，进行分布式计算。 7. **图算法优化**：讨论了针对特定硬件和数据分布的图算法性能优化技术。 8. **案例研究**：通过实际案例展示了图算法在知识图谱、社交网络分析、推荐系统和欺诈检测等领域的应用。这本书的第一版发布于2019年5月，作者在发布后不断更新和优化内容，确保读者能获取最新的图算法实践知识。无论你是数据科学家还是软件开发者，深入理解并应用这些图算法都能提升你在知识图谱领域的工作效率和解决问题的能力。

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Foreword

What do the following things all have in common: marketing attribution analysis,

anti-money laundering (AML) analysis, customer journey modeling, safety incident

causal factor analysis, literature-based discovery, fraud network detection, internet

search node analysis, map application creation, disease cluster analysis, and analyzing

the performance of a William Shakespeare play. As you might have guessed, what

these all have in common is the use of graphs, proving that Shakespeare was right

when he declared, “All the world’s a graph!”

Okay, the Bard of Avon did not actually write graph in that sentence, he wrote stage.

However, notice that the examples listed above all involve entities and the relation‐

ships between them, including both direct and indirect (transitive) relationships.

Entities are the nodes in the graph—these can be people, events, objects, concepts, or

places. The relationships between the nodes are the edges in the graph. Therefore,

isn’t the very essence of a Shakespearean play the active portrayal of entities (the

nodes) and their relationships (the edges)? Consequently, maybe Shakespeare could

have written graph in his famous declaration.

What makes graph algorithms and graph databases so interesting and powerful isn’t

the simple relationship between two entities, with A being related to B. After all, the

standard relational model of databases instantiated these types of relationships in its

foundation decades ago, in the entity relationship diagram (ERD). What makes

graphs so remarkably important are directional relationships and transitive relation‐

ships. In directional relationships, A may cause B, but not the opposite. In transitive

relationships, A can be directly related to B and B can be directly related to C, while A

is not directly related to C, so that consequently A is transitively related to C.

With these transitivity relationships—particularly when they are numerous and

diverse, with many possible relationship/network patterns and degrees of separation

between the entities—the graph model uncovers relationships between entities that

otherwise may seem disconnected or unrelated, and are undetected by a relational

xiii

database. Hence, the graph model can be applied productively and effectively in many

network analysis use cases.

Consider this marketing attribution use case: person A sees the marketing campaign;

person A talks about it on social media; person B is connected to person A and sees

the comment; and, subsequently, person B buys the product. From the marketing

campaign manager’s perspective, the standard relational model fails to identify the

attribution, since B did not see the campaign and A did not respond to the campaign.

The campaign looks like a failure, but its actual success (and positive ROI) is discov‐

ered by the graph analytics algorithm through the transitive relationship between the

marketing campaign and the final customer purchase, through an intermediary

(entity in the middle).

Next, consider an anti-money laundering (AML) analysis case: persons A and C are

suspected of illicit trafficking. Any interaction between the two (e.g., a financial trans‐

action in a financial database) would be flagged by the authorities, and heavily scruti‐

nized. However, if A and C never transact business together, but instead conduct

financial dealings through safe, respected, and unflagged financial authority B, what

could pick up on the transaction? The graph analytics algorithm! The graph engine

would discover the transitive relationship between A and C through intermediary B.

In internet searches, major search engines use a hyperlinked network (graph-based)

algorithm to find the central authoritative node across the entire internet for any

given set of search words. The directionality of the edge is vital in this case, since the

authoritative node in the network is the one that many other nodes point at.

With literature-based discovery (LBD)—a knowledge network (graph-based) applica‐

tion enabling significant discoveries across the knowledge base of thousands (or even

millions) of research journal articles—“hidden knowledge” is discovered only

through the connection between published research results that may have many

degrees of separation (transitive relationships) between them. LBD is being applied to

cancer research studies, where the massive semantic medical knowledge base of

symptoms, diagnoses, treatments, drug interactions, genetic markers, short-term

results, and long-term consequences could be “hiding” previously unknown cures or

beneficial treatments for the most impenetrable cases. The knowledge could already

be in the network, but we need to connect the dots to find it.

Similar descriptions of the power of graphing can be given for the other use cases lis‐

ted earlier, all examples of network analysis through graph algorithms. Each case

deeply involves entities (people, objects, events, actions, concepts, and places) and

their relationships (touch points, both causal and simple associations).

When considering the power of graphing, we should keep in mind that perhaps the

most powerful node in a graph model for real-world use cases might be “context.”

Context may include time, location, related events, nearby entities, and more. Incor‐

xiv | Foreword

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图算法实战：Apache Spark与Neo4j平台的应用

neo4j_community_4.4.2_windows.zip 下载可用，社区版本的

neo4j_community_3.5.30

https://neo4j.com/developer/java/#_neo4j_jdbc_driver下载

KeyError: neo4j_version

创建主目录环境变量NEO4J_HOME，并把主目录设置为变量值。

from ..neo.neo_config import NEO4J_CONFIG ImportError: attempted relative import with no known parent package

ModuleNotFoundError: No module named 'neo4j_tool'

ImportError: cannot import name 'INT64_MIN' from 'neo4j.packstream' (C:\Users\dev\Anaconda3\lib\site-packages\neo4j\packstream\__init__.py)

keyerror: 'neo4j_version'

java.lang.ClassNotFoundException: org.neo4j.driver.Session

org.neo4j.cypherdsl

neo4j C++ Win

错误: 找不到或无法加载主类 org.neo4j.server.startup.Neo4jCommand 原因: java.lang.ClassNotFoundException: org.neo4j.server.startup.Neo4jCommand

java.lang.ClassNotFoundException: org.neo4j.server.startup.Neo4jCommand

neo4j安装gds

错误: 找不到或无法加载主类 org.neo4j.server.startup.Neo4jCommand是什么原因

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ImportError: cannot import name 'INT64_MIN' from 'neo4j.packstream' (C:\Users\dev\Anaconda3\lib\site-packages\neo4j\packstream\init.py)