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Use Case: Deep Graph Traversal


In this chapter, we'll be covering selective scenarios that Ultipa Graph excels in. In general, Ultipa Graph can be used for all graph computing use cases, especially in those cases that deep data correlations (and/or real-time computing) are sought after, but there is no need to limit Ultipa Graph to those kinds of scenarios only.

Keep in mind the following characteristics of Ultipa Graph:

  • Super-fast graph search:  High QPS/TPS. (100x to competitions)
  • Super-deep search against any type of graph.  (10x deeper)
  • Highly concurrent graph system: Extremely high throughput. (10x higher concurrency)
  • Relatively small run-time in-memory footprint. (50% less RAM)
  • Very stable graph system.
  • Highly visualized graph database manager & high-performance KG-embedded front-end.

There are other features, the list can be very long, but for now, the aforementioned 5 things are to bear in your mind, and in the benchmarks article, more concrete and quantitative reports are shared.

Generally speaking, Ultipa Graph system serves at least the following domains:

  • BFSI sector:  Banking, Financial, Security, and Insurance sectors, with scenarios like anti-fraud, asset management, anti-money-laundering, risk control, risk budgeting and risk management, IT auditing, and more.
  • Telco carrier: customer 360, smart recommendation, anti-fraud, network monitoring and management with knowledge graph, and etc.
  • IoT: The IoT problem is both big data and fast data, and to dig more value out of the IoT network, network analytics is a must-have/must-do, it makes every sense to leverage graph database for this purpose. 
  • Supply Chain Management: Supply chain tends to form a gigantic network and to do data analytics against this network, graph is your best friend!
  • Internet sector:  Features like knowledge graph, smart search & recommendation, chatbot, fraud-detection and etc.

We have collected a few use cases for your reference, they are:

  • Deep Graph Traversal – UBO Identification.
  • Fraud Prevention – Corporate Guarantee-Chain Detection
  • Fraud Detection – Real-Time Decision Making
  • Knowledge Graph & Real-time Computing
  • Anti-Fraud – Real-time Fraud Call Detection
  • Crime Fighting – Real-time Crime-Ring/Network Detection(To Be Done)
  • Other assorted use cases.

Use Case: Deep Graph Traversal, UBO Finding

Technically speaking, DGT (Deep Graph Traversal) is not a use case, it is a unique feature by graph computing system. In high-performance systems like Ultipa Graph, real-time DGT can be very beneficial to easily solve real-world business challenges.

Here is one such challenge faced by local, state-wide and federal authorities in San Francisco, in the past decade San Francisco’s real-estate properties that were supposedly allocated to low-income local families have been bought up by LLCs (Limited Liability Companies) that are hard to trace their UBOs (Ultimate Beneficial Owners). The issues were so prevalent that this has become a major concern, government agencies like IRS (Internal Revenue Services) and local law enforcement are interested in understanding what parties are hiding behind these LLCs, manual exploration process can be very labor-intensive and time-consuming, because the eventual UBO parties may hide many hops (layers) behind the surface LLCs, and often times these UBOs intentionally hide and cross-owning their shares, making the overall owning structures highly complicated. An automated white-box AI solution is desired.

Similar cases are also popular in other markets, for instance, China has seen the rise of Three-Cha (namely Tianyancha, Qichacha and Qixinbao), the top players that offer semi-automatic business background investigation services online).

A Company’s Ownership Network Topology

To understand a business entity’s ownership structure, the most intuitive way is to present the relevant data in a graphical manner, a.k.a in a correlated fashion. If a person acts as the legal representation of a company, in a graph setup, this is an edge connecting two nodes, one node being the person, pointing at the other node which is the company, and the edge (relationship) is labeled “Legal”.

Assuming we have collected all data of a business entity, starting from the business entity node, recursively, all entities that are linked with it can be retrieved and form a sub-graph (see the next diagram). Note that, the resulting collection of nodes and edges may form a graph instead of a tree because a tree does NOT have circles, but a graph may, and this reflects real-world scenarios better because business entitles and persons may form circles or cross-investment/owning business structures, which can NOT be represented with a hierarchical tree structure.

UBOs of A Business Entity that are 10+ Hops Away

In real-world setup, it is NOT uncommon to see the eventual owners (aka Ultimate Business Owners, or majority-stake holders) that are many hops away from the business entity that is being examined (the red star). A traditional RDBMS or document database (or even most graph databases) is NOT capable of addressing such exploration in a fast and timely fashion. The above diagram shows that the UBOs are at least 10 hops away, sometimes the computation complexity is overwhelming for non-real-time graph databases, this is because, each business entity can have many owners (business or personnel), the mathematical calculation can be exponentially complex every time we dig a hop deeper. Let’s do the simple math here:

  • Assuming there are 25 owners for an entity;
  • If we are to dig 5 layers deep:  25 * 25 * 25 * 25 * 25 = 9,765,625 (~)10M
  • If we are to dig 10 layers:  1014 = 100 trillion

If we are NOT equipped with the right data structural, algorithmic, and architectural system, we are not going to solve these challenges.

Fortunately, Ultipa Graph is designed to address such challenges with extremely high efficiency and efficacy via high-concurrency data structure, architecture, and algorithm hyper-parallelization. We identify the UBOs in pure real-time, oftentimes in micro-second grade, because we deep-traverse the graph many times (100x or more) faster than other graph systems, not to mention that RDBMS are totally incapable of addressing such scenarios. On the other hand, micro-second turn-around time means much higher concurrency and system-level throughput, it’s 1,000x times higher concurrency to systems claiming with millisecond turnaround time!

Now, we know that deep traversal, in general, can identify the shareholders of a business no matter how far away they are from the starting business entity, and sometimes, there are thousands of them. It would always be beneficial to know the top 5 or 10 of the owners or parties who have more than 5,10 or 25% stake of ownership – which are considered UBOs.

UBOs Identification and Ownership Calculations

The above diagram shows that, with Ultipa Manager, the web-based Ultipa Graph Database management front-end, and UQL, the super-powerful, intuitive, and easy-to-use query language of Ultipa Graph, UBOs of a business entity can easily be identified with laser-like mathematical precision and graphically presented (highlighted) for the minimal cognitive load.

UBOs Identification and Ownership Calculations

Supplementary Notes: The above diagram shows that the UBOs are hiding 4 hops away, and they use a cross-owning structure via several layers of business entities, either intentional or unintentionally to avoid direct ownership. In real-world setup, we’ve seen many large enterprises having thousands of UBOs, they form a gigantic subgraph that hinders the investigation, being able to quickly identify the top stakeholders is of great interest to both market regulators, analysts, or law enforcement.

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