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Date: Tue, 28 Jul 2015 07:38:04 +0000 (UTC)
From: "Vasia Kalavri (JIRA)" <jira@apache.org>
To: issues@flink.apache.org
Message-ID: <JIRA.12849353.1438011739000.304868.1438069084727@Atlassian.JIRA>
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Subject: [jira] [Commented] (FLINK-2411) Add basic graph summarization
 algorithm
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    [ https://issues.apache.org/jira/browse/FLINK-2411?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14643995#comment-14643995 ] 

Vasia Kalavri commented on FLINK-2411:
--------------------------------------

Hi [~mju]!

Thank you for the detailed description ^^ 
This would be a great addition to the Gelly library!
I agree with you, let's start with a simple algorithm as you describe it.
I'll assign this issue to you. Let us know if you have questions!

> Add basic graph summarization algorithm
> ---------------------------------------
>
>                 Key: FLINK-2411
>                 URL: https://issues.apache.org/jira/browse/FLINK-2411
>             Project: Flink
>          Issue Type: New Feature
>          Components: Gelly
>    Affects Versions: master
>            Reporter: Martin Junghanns
>            Priority: Minor
>
> Graph summarization determines a structural grouping of similar vertices and edges to condense a graph and thus helps to uncover insights about patterns hidden in the graph. It can be used in OLAP-style operations on the graph and is similar to group by in SQL but on the graph structure instead of rows.
>  
> The graph summarization operator represents every vertex group by a single vertex in the summarized graph; edges between vertices in the summary graph represent a group of edges between the vertex group members of the original graph. Summarization is defined by specifying grouping keys for vertices and edges, respectively.
> One publication that presents a Map/Reduce based approach is "Pagrol: Parallel graph olap over large-scale attributed graphs", however they pre-compute the graph-cube before it can be analyzed. With Flink, we can give the user an interactive way of summarizing the graph and do not need to compute the  cube beforehand.
> A more complex approach focuses on summarization on graph patterns  "SynopSys: Large Graph Analytics in the SAP HANA Database Through Summarization".
> However, I want to start with a simple algorithm that summarizes the graph on vertex and optionally edge values and additionally stores a count aggregate at summarized vertices/edges.
> Consider the following two examples (e.g., social network with users from cities and friendships with timestamp):
>  
> h4. Input graph:
>  
> Vertices (id, value):
> (0, Leipzig)
> (1, Leipzig)
> (2, Dresden)
> (3, Dresden)
> (4, Dresden)
> (5, Berlin)
> Edges (source, target, value):
> (0, 1, 2014)
> (1, 0, 2014)
> (1, 2, 2013)
> (2, 1, 2013)
> (2, 3, 2014)
> (3, 2, 2014)
> (4, 0, 2013)
> (4, 1, 2015)
> (5, 2, 2015)
> (5, 3, 2015)
> h4. Output graph (summarized on vertex value):
> Vertices (id, value, count)
> (0, Leipzig, 2) // "2 users from Leipzig"
> (2, Dresden, 3) // "3 users from Dresden"
> (5, Berlin, 1) // "1 user from Berlin"
> Edges (source, target, count) 
> (0, 0, 2) // "2 edges between users in Leipzig"
> (0, 2, 1) // "1 edge from users in Leipzig to users in Dresden"
> (2, 0, 3) // "3 edges from users in Dresden to users in Leipzig"
> (2, 2, 2) // "2 edges between users in Dresden"
> (5, 2, 2) // "2 edges from users in Berlin to users in Dresden"
> h4. Output graph (summarized on vertex and edge value):
> Vertices (id, value, count)
> (0, Leipzig, 2)
> (2, Dresden, 3)
> (5, Berlin, 1)
> Edges (source, target, value, count) 
> (0, 0, 2014, 2) // ...
> (0, 2, 2013, 1) // ...
> (2, 0, 2013, 2) // "2 edges from users in Dresden to users in Leipzig with timestamp 2013"
> (2, 0, 2015, 1) // "1 edge from users in Dresden to users in Leipzig with timestamp 2015"
> (2, 2, 2014, 2) // ...
> (5, 2, 2015, 2) // ...
> I've already implemented two versions of the summarization algorithm in our own project [Gradoop|https://github.com/dbs-leipzig/gradoop], which is a graph analytics stack on top of Hadoop + Gelly/Flink with a fixed data model. You can see the current WIP here: 
> 1 [Abstract summarization|https://github.com/dbs-leipzig/gradoop/blob/%2345_gradoop_flink/gradoop-flink/src/main/java/org/gradoop/model/impl/operators/Summarization.java]
> 2 [Implementation using cross|https://github.com/dbs-leipzig/gradoop/blob/%2345_gradoop_flink/gradoop-flink/src/main/java/org/gradoop/model/impl/operators/SummarizationCross.java]
> 3 [Implementation using joins|https://github.com/dbs-leipzig/gradoop/blob/%2345_gradoop_flink/gradoop-flink/src/main/java/org/gradoop/model/impl/operators/SummarizationJoin.java]
> 4 [Tests|https://github.com/dbs-leipzig/gradoop/blob/%2345_gradoop_flink/gradoop-flink/src/test/java/org/gradoop/model/impl/EPGraphSummarizeTest.java]
> 5 [TestGraph|https://github.com/dbs-leipzig/gradoop/blob/%2345_gradoop_flink/dev-support/social-network.pdf]
> I would basically use the same implementation as in 3 in combination with KeySelectors to select the grouping keys on vertices and edges.
> As you can see in the example, each vertex in the resulting graph has a vertex id that is contained in the original graph. This id is the smallest id among the grouped vertices (e.g., vertices 2, 3 and 4 represent Dresden, so 2 is the group representative). The latter is necessary to correctly assign the summarized edges. Maybe there is a smarter way to do it of which I did not think of yet.
> I would like contribute this to Flink and of course, if you have any suggestions/improvements or do not want this at all (hopefully not), please let me know.


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