Data for many problem domains are naturally well represented as graphs, and graph analytics has thus become an important tool in many areas of business and science alike. In order to support increasingly large data sets and thus graph with increasingly large sets of vertices and edges, scalable graph analytics engines like neo4j partition and distribute graph vertices across compute nodes, leveraging hardware parallelism by processing queries in a distributed manner. In short, queries are then propagated across compute nodes following the query logic and edges between the respective vertices. Such systems are also capable of supporting concurrent queries on overlapping subgraphs and thus sets of compute edges with some minimal synchronization. This poster leverages novel advances on totally ordered fault-tolerant communication for real-time processing on large graphs. Total order multicast distinguishes between messages with different sets of destination processes. More precisely, as opposed to total order broadcast where all messages are indifferently issued to an entire group of processes, total order multicast distinguishes between different subgroups of processes, that can be addressed individually by processes issuing messages.