Linkurious Enterprise 2.10: edge grouping and dynamic node sizing

We are happy to share that Linkurious Enterprise 2.10 is available in beta. This new version ships with the following new capabilities: 

  • Edge grouping
  • Dynamic sizing of nodes
  • Ability to use edges and current users as input in Query Templates

This release also brings changes such as improved support for CosmosDB, compatibility with Elasticsearch 7.X, information about users’ last active date, additional information for closed alerts, and removal of filtered out nodes and edges in exports.

Please note that this release is a beta version. Read the changelog and backup before upgrading.

Edge grouping

If you have nodes connected by multiple relationships, it’s now possible to group those relationships to unclutter your visualization and grasp the bigger picture.

Depending on the stage of your investigation:

  • You may either want to look at the big picture and visualize that 2 persons are engaged in a transactional relationship without seeing the full detail of the transactions
  • Or you may want to visualize and individually inspect each transaction between 2 persons to understand how they are connected.

Analysts can now dynamically change from one mode of visualization to the other by grouping or ungrouping relationships by type.

Here we start with 2 clients that are connected by multiple transactions. After exploring their surroundings, the visualization begins to become more cluttered by multiple relationships. These relationships are grouped to make things easier to understand.

If in the past you’ve had questions or issues regarding whether to model certain information as multiple individual relationships or as a single aggregated relationship, we encourage you to try how the grouping feature in Linkurious Enterprise 2.10 can help you.

Native incremental indexing with Elasticsearch

Linkurious Enterprise provides multiple options to allow users to do full text search on their graph: AzureSearch, Neo4jSearch (i.e. native Neo4j full-text search), and Elasticsearch, a highly popular and scalable open-source search engine. 

For a search engine to function, it needs an up-to-date index. In the case of Linkurious Enterprise, this index needs to be synchronized with the content of a graph database. For AzureSearch or Neo4jSearch, the synchronization is orchestrated natively by Azure and Neo4j. But in the case of Elasticsearch we have had to rely on a plugin called “neo4j-to-elasticsearch” that was responsible for sending changes in Neo4j to Elasticsearch for indexation.

The use of neo4j-to-elasticsearch involved downloading and installing the plugin. To make this process smoother and easier, Linkurious Enterprise 2.10 introduces a built-in incremental indexation. Without leaving the Linkurious Enterprise UI, it’s possible to set up an indexation approach that will deliver up-to-date results.

The incremental indexation runs at the time interval of your choice. New changes to the data (added or modified properties) are identified and added to the search index in Elasticsearch. These changes are available in the search results of Linkurious Enterprise immediately after the next incremental indexation is finished.

For the incremental indexing to work, your database administrator will need to add a “last edit date” to your data.

The incremental indexing is currently in beta. It doesn’t support the indexing of edges or the removal of nodes and edges from the search index. In the future the incremental indexation will replace the neo4j-to-elasticsearch approach which will no longer be supported. If you are currently using neo4j-to-elasticsearch for incremental indexing, we recommend that you keep using it together with Neo4j v3.X for now. Please note that neo4j-to-elasticsearch is currently not compatible with Neo4j v4.X. If you want to move to Neo4j v4.X, we recommend that you reach out to support@linkurio.us to discuss the best approach.

Use edges and current users as input in Query Templates

Query Templates turns graph queries into easy to use buttons and forms. From finding a shortest path by taking into account specific constraints, geotagging an address via the Google Maps API or finding the dependencies of a node, Query Templates automate investigation workflows.

With Linkurious Enterprise 2.10, it’s now possible to use an edge or a group of edges as an input for a Query Template.

A Query Template quickly identifies all the transactions that were done within 24 hours of the transaction we’re inspecting.

You can now also use the “current user” as a parameter for a Query Template. For example, if you have a query template that edits a node (e.g. change the content of a “status” property to “suspicious”), you can also update the node with the person responsible for the edit (e.g. update an “Edited by” property with the name of the user who has used the Query Template).

Dynamic sizing for nodes and edges

The size of nodes and edges can be used as a visual cue to attract the analysts’ attention to certain parts of a visualization. With Linkurious Enterprise 2.10, it’s possible to dynamically size nodes or edges based on numerical properties. With a numerical “amount” property, for example, the higher the amount the bigger the node in the visualization, and the smaller the “amount”, the smaller the node.

As we explore the startup ecosystem, the size of the nodes helps identify the companies with the highest number of funding rounds. Criteo is preeminent in the visualization but becomes smaller when companies such as Clear2Pay or Talend are added.

With dynamic sizing, the size of nodes or edges are based on the specific context of your visualization. In a glimpse you can look at the sizes of different edges to identify the biggest transaction. It doesn’t matter if that biggest transaction is actually small compared to all the transactions in your database, the transaction will be sized based on what you’re looking at to facilitate the analysis.

In order to apply dynamic sizing, you need to edit the Default Styles of your data source.

In addition to dynamic sizing, it’s still possible to manually map different values to different sizes within the Linkurious Enterprise UI or to manually define size rules via the Linkurious Enterprise’s configuration file.

Changes specific to Cosmos DB

For technical reasons we could not support CosmosDB with Linkurious Enterprise 2.8. We were able to make changes so that CosmosDB is supported in Linkurious Enterprise 2.9 and 2.10.

Changes specific to Neo4j

As part of the release of Linkurious Enterprise v2.10, v2.9.7 is now the official stable version. Both versions support Neo4j v4 including the new multi-tenancy feature.

Neo4j v4.1 has introduced performance improvements in Neo4jSearch. We recommend clients using Neo4jSearch to upgrade to Neo4j v4.1 to benefit from faster search results.

Other changes in Linkurious Enterprise v2.10

Linkurious Enterprise also comes with multiple minor improvements:

  • Additional information for closed alerts: when looking at a closed alert, you can see when it was closed and by whom. It’s now easier to know who to coordinate with to learn more about a closed alert.
  • User’s last active date: you can see the last login date of users in the admin dashboard. It’s now easier to identify users who are either actively using the software or those who are not.
  • Compatibility with Elasticsearch 7.X for customers using neo4j-to-elasticsearch: customers using neo4j-to-elasticsearch for their search strategy are no longer limited to Elasticsearch 5.X and can upgrade to Elasticsearch 7.X. Support for Elasticsearch 7.X will also be added in the future for other search strategies.

To learn more about Linkurious Enterprise 2.10, register for our upcoming webinar on November 19th. We will do an exclusive live demo of the features mentioned above.

Linkurious Enterprise 2.10 Webinar Registration

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