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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2015/07/03 04:06:04 UTC
[jira] [Updated] (SPARK-8497) Graph Clique(Complete Connected
Sub-graph) Discovery Algorithm
[ https://issues.apache.org/jira/browse/SPARK-8497?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Xiangrui Meng updated SPARK-8497:
---------------------------------
Assignee: Fan Jiang
> Graph Clique(Complete Connected Sub-graph) Discovery Algorithm
> --------------------------------------------------------------
>
> Key: SPARK-8497
> URL: https://issues.apache.org/jira/browse/SPARK-8497
> Project: Spark
> Issue Type: New Feature
> Components: GraphX, ML, MLlib, Spark Core
> Reporter: Fan Jiang
> Assignee: Fan Jiang
> Labels: features
> Original Estimate: 72h
> Remaining Estimate: 72h
>
> In recent years, social network industry has high demand on Complete Connected Sub-Graph Discoveries, so does Telecom. Similar as the graph connection from Twitter, the calls and other activities from telecoms world form a huge social graph, and due to the nature of communication method, it shows the strongest inter-person relationship, the graph based analysis will reveal tremendous value from telecoms connections.
> We need an algorithm in Spark to figure out ALL the strongest completely connected sub-graph (so called Clique here) for EVERY person in the network which will be one of the start point for understanding user's social behaviour.
> In Huawei, we have many real-world use cases that invovle telecom social graph of tens billion edges and hundreds million vertices, and the cliques will be also in tens million level. The graph will be a fast changing one which means we need to analyse the graph pattern very often (one result per day/week for moving time window which spans multiple months).
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