Question on writing batch synchronized incremental graph algorithms

[Kaan Sancak <ka...@gmail.com>]

Hi all,
I have been trying to write batch-synchronized  incremental graph algorithms. More specifically, I want to run an increment algorithm on a given data-set and when a new batch arrives, I want to start the algorithm from last snapshot, and run the algorithm on the vertices that are effected by the new batch. (Assuming that each batch contains a set of insertions/deletions to the graph, effected vertices are the vertices whose neighbor set is effected by the insertions/deletions).

I found out a paper[1] which mentions about a framework called GraphTau built on top of GraphX, and I also found out couple of Spark Summits[2] mention future code release. I have looked but couldn’t find any code available to public. 
Is there anyone who can inform me about the subject? Or are there any features on GraphX currently available for me to write such algorithms.

[1] Time-Evolving Graph Processing at Scale: https://www.researchgate.net/publication/305661018_Time-evolving_graph_processing_at_scale <https://www.researchgate.net/publication/305661018_Time-evolving_graph_processing_at_scale> .

[2] Spark Summit 2017: https://www.slideshare.net/SparkSummit/timeevolving-graph-processing-on-commodity-clusters-spark-summit-east-talk-by-anand-iyer <https://www.slideshare.net/SparkSummit/timeevolving-graph-processing-on-commodity-clusters-spark-summit-east-talk-by-anand-iyer>

Best
Kaan