2020 Neo4j图算法实战教程：Apache Spark与实践

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"《Neo4j图算法》是一本由Mark Needham和Amy E. Hodler合著的实用指南，于2020年发布，针对最新版本的Neo4j数据库系统深入探讨图网络算法。该PDF文档提供了一手的前沿技术学习资料，涵盖了在Apache Spark和Neo4j这样的平台中实施图形算法的实际例子。作者以其丰富的经验和专业知识，带领读者理解并应用图论在各种场景中的关键作用，如社交网络分析、推荐系统、路径查找和社区检测等。本书适合对图数据结构和算法感兴趣的开发者、数据科学家以及研究者，无论他们是否熟悉Neo4j。书中不仅介绍了理论概念，还提供了实战操作步骤，帮助读者掌握如何利用Neo4j高效处理复杂的图数据。通过实例，读者将学会如何设计查询、优化性能、理解和解决图算法在实际项目中的挑战。版权方面，本书受2019年Amy Hodler和Mark Needham的共同版权保护，所有权利保留。本书印刷在美国，并由O'Reilly Media出版，强调了在线购买的便捷性，鼓励教育、商业或销售推广用途。首次出版日期是2019年5月，之后进行了多次修订，确保内容的时效性和准确性。本书的编排包括了诸如Acquisitions Editor、Development Editor、Production Editor等专业团队的合作，确保了质量控制和内容的专业呈现。从封面设计到目录编制，从插图到校对，每个环节都体现了作者对细节的关注和对读者体验的尊重。通过阅读《Neo4j图算法》，读者不仅可以提升在图数据库领域的技能，还能了解到如何在现实世界问题中巧妙运用图算法，推动业务创新。对于想要在大数据时代掌握核心竞争力的IT专业人士来说，这是一本不可多得的参考资料。"

Foreword

Wha

t do the following things all ha

ve 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 a

ttribution use case: person A sees the marketing cam

paign;

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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2020 Neo4j图算法实战教程：Apache Spark与实践

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