Using PhoneGraph to build trust in a B2B market

In a business-to-business setting, trust commands a huge premium. Creating a process driven system that helps users identify trustworthy business partners is a key issue that needs to be solved in any B2B market place. Alibaba on its B2B marketplace provides a trust seal service which businesses actually buy to look “trusted” on the portal. Indiamart has the same approach. In real estate’s broker to broker market it was no different.


Real estate portals and mobile apps have revolutionized the traditional business model which has been in practice for decades. It has given the much required adrenaline push to the industry by connecting owners, buyers, tennants ,real estate agents and brokers.

However, even with the inclusion of multiple technology solutions genuineness of the brokers has remained an unsolved problem in the real estate industry. Identifying a fake listing is not only attributed to the properties of the listing such as price, locality and amenities but also to the credibility of the person who posted it. We at BroEx take this problem very seriously and have come up with our own magical algorithm to solve this. We have put our best efforts towards creating a clean broker to broker network and have used technology as a tool to identify the genuineness of the brokers in our network.

So how is BroEx, by using smart technologies, taking care of this major issue?

Once a user installs the app, we request for their contact information that includes their phonebook contacts. This helps us in creating a relational graph of “who knows who” on our platform. At the same time, it also benefits brokers to know their first degee and second degree connections on Broex.

For every contact that is stored in user’s phone book, we create a node in the already existing relational map. These nodes connect to all the other nodes that are there in user’s contact list. With increasing number of brokers joining BroEx daily, our the 85,000+ strong broker network keeps learning more and more about the strength of connections between all the broker nodes. As a result, the system becomes smarter with every new broker joining the network.

How this very simple magic works?

Let’s assume a new user Mr. X joins BroEx. The user connection intelligence graph that already has information of 85,000+ brokers in India is able to answer to the following questions:

  • How many brokers know Mr. X (we call this #Recognitions)
  • How many brokers does Mr. X know (we call this #Contacts)
  • By what degree are Mr. X and Mr. Y connected (we call this #Degree of Connection)

As the number of recognitions, contacts and higher number of 1st and 2nd degree connnetion of Mr. X increase he becomes a more reliable broker on the platform. The system reguarly scans for unreliable brokers and locks their profiles in case it finds the profile suspicious.

Which technology we are embracing for this purpose and why?

Neo4j is a graph database management system developed by Neo Technology, Inc. Described by its developers as an ACID-compliant transactional database with native graph storage and processing.

Neo4j helps us to ascertain that each piece of data in the graph has an explicit connection to everything it’s related to. This means the database can ignore anything that’s not connected, rather than having to trawl through all of the data in order to determine what is connected. This results in unparalleled speed and scale.

Relationships are first-class citizens of the graph data model, unlike other database management systems, which require us to infer connections between entities using special properties such as foreign keys, or out-of-band processing like map-reduce. By assembling the simple abstractions of nodes and relationships into connected structures, graph databases enable us to build sophisticated models that helps us to do the broker to broker connection magic.

We use relational database (Postgres) for storing most of our structured data. Postgres could have been our first choice of database for storing relationship between users on BroEx platform. Relational databases are good at storing highly structured data with predetermined columns of a table. So it becomes difficult to maintain sparse adjacency matrix in a relational database. Also queries will not be optimized for queries like finding relations, degree of connection etc.

Hence relational database was out of question. We started exploring graph databases which are storage system that provides index-free adjacency. Graph databases contains for every element direct link to its adjacent elements. Hence we can easily query relations, degree of connections etc.

There are various graph databases available. We chose neo4j because it has largest and most active community support. Also query language of neo4j is easier to understand and write than other graph databases we considered.

Team BroEx

BroEx is the largest go-to application platform for Real Estate Brokers to connect with each other to network and collaborate with each other to fulfill their business requirements reliably and quickly.

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