Re: Apache Flink 1.1.4 - Gelly - LocalClusteringCoefficient - Returning values above 1?

Posted by Miguel Coimbra on
URL: http://deprecated-apache-flink-user-mailing-list-archive.369.s1.nabble.com/Apache-Flink-1-1-4-Gelly-LocalClusteringCoefficient-Returning-values-above-1-tp11187p11195.html

Hello Vasia and Greg,

Thank you for the feedback!

I am probably misusing the Gelly API in some way, but I thought I could run the undirected version after calling getUndirected()?

While not going into the concept of local clustering coefficients, I thought that from a Gelly API point-of-view, both my code and data set were properly established.

However:

- I believe that the graph was already undirected;

- I am getting NaN results after executing the algorithm.

This is the code I am using to obtain an (undirected) graph instance upon which I call LocalClusteringCoefficient:


import org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient.Result;
import org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient;

/** other imports and method definitions **/

// Generate edge tuples from the input file.
final DataSet<Tuple2<LongValue, LongValue>> edgeTuples = env.readCsvFile(inputPath)
    .fieldDelimiter("\t") // node IDs are separated by spaces
    .ignoreComments("#")  // comments start with "%"
    .types(LongValue.class, LongValue.class); 

// Generate actual Edge<Long, Double> instances.
@SuppressWarnings("serial")
final DataSet<Edge<LongValue, Double>> edges = edgeTuples.map(
    new MapFunction<Tuple2<LongValue, LongValue>, Edge<LongValue, Double>>() {
        @Override
        public Edge<LongValue, Double> map(Tuple2<LongValue, LongValue> arg0) throws Exception {
            return new Edge<LongValue, Double>(arg0.f0,  arg0.f1, 1.0d);
        }
    });

// Generate the basic graph.
@SuppressWarnings("serial")
final Graph<LongValue, Double, Double> graph = Graph.fromDataSet(
    edges,
    new MapFunction<LongValue, Double>() {
        @Override
        public Double map(LongValue arg0) throws Exception {

            // For testing purposes, just setting each vertex value to 1.0.

            return 1.0;
        }
    },
    env).getUndirected();

// Execute the LocalClusteringCoefficient algorithm.

final DataSet<Result<LongValue>> localClusteringCoefficients = graph.run(new LocalClusteringCoefficient<LongValue, Double, Double>());

// Get the values as per Vasia's help:
@SuppressWarnings("serial")
DataSet<Double> CLUSTERING_COEFFICIENTS = localClusteringCoefficients.map(new MapFunction<Result<LongValue>, Double>() {
    @Override
    public Double map(Result<LongValue> arg0) throws Exception {
        return arg0.getLocalClusteringCoefficientScore();
    }
});

I believe this is the correct way to get a DataSet<Double> of coefficients from a DataSet<Result<LongValue>> ?

Among the coefficients are a lot of NaN values:

CLUSTERING_COEFFICIENTS.print();

NaN
0.0
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN

Apologies for the verbosity in advance, but just to provide detail, printing the graph edges yields this (notice that for each pair or vertices there are two links, which are original and the reverse version derived from getUndirected()).

Greg: I therefore believe the graph is undirected:

graph.getEdgesAsTuple3().print();
(5113,6008,1.0)
(6008,5113,1.0)
(5113,6774,1.0)
(6774,5113,1.0)
(5113,32938,1.0)
(32938,5113,1.0)
(5113,6545,1.0)
(6545,5113,1.0)
(5113,7088,1.0)
(7088,5113,1.0)
(5113,37929,1.0)
(37929,5113,1.0)
(5113,26562,1.0)
(26562,5113,1.0)
(5113,6107,1.0)
(6107,5113,1.0)
(5113,7171,1.0)
(7171,5113,1.0)
(5113,6192,1.0)
(6192,5113,1.0)
(5113,7763,1.0)
(7763,5113,1.0)
(9748,5113,1.0)
(5113,9748,1.0)
(10191,5113,1.0)
(5113,10191,1.0)
(6064,5113,1.0)
(5113,6064,1.0)
(6065,5113,1.0)
(5113,6065,1.0)
(6279,5113,1.0)
(5113,6279,1.0)
(4907,5113,1.0)
(5113,4907,1.0)
(6465,5113,1.0)
(5113,6465,1.0)
(6707,5113,1.0)
(5113,6707,1.0)
(7089,5113,1.0)
(5113,7089,1.0)
(7172,5113,1.0)
(5113,7172,1.0)
(14310,5113,1.0)
(5113,14310,1.0)
(6252,5113,1.0)
(5113,6252,1.0)
(33855,5113,1.0)
(5113,33855,1.0)
(7976,5113,1.0)
(5113,7976,1.0)
(26284,5113,1.0)
(5113,26284,1.0)
(8056,5113,1.0)
(5113,8056,1.0)
(10371,5113,1.0)
(5113,10371,1.0)
(16785,5113,1.0)
(5113,16785,1.0)
(19801,5113,1.0)
(5113,19801,1.0)
(6715,5113,1.0)
(5113,6715,1.0)
(31724,5113,1.0)
(5113,31724,1.0)
(32443,5113,1.0)
(5113,32443,1.0)
(10370,5113,1.0)
(5113,10370,1.0)

Any insight into what I may be doing wrong would be greatly appreciated.

Thanks for your time,

Kind regards,

On 20 January 2017 at 19:31, Greg Hogan <[hidden email]> wrote:

Hi Miguel,

The '--output print' option describes the values and also displays the local clustering coefficient value.

You're running the undirected algorithm on a directed graph. In 1.2 there is an option '--simplify true' that will add reverse edges and remove duplicate edges and self-loops. Alternatively, it looks like you could simply add reverse edges to your input file (with an optional ' | sort | uniq' following):

$ cat edges.txt | awk ' { print $1, $2; print $2, $1 } '

The drivers are being reworked for 1.3 to better reuse code and options which will better support additional drivers and algorithms and make documentation simpler.

Greg

On Fri, Jan 20, 2017 at 2:06 PM, Vasiliki Kalavri <[hidden email]> wrote:

Hi Miguel,

the LocalClusteringCoefficient algorithm returns a DataSet of type Result, which basically wraps a vertex id, its degree, and the number of triangles containing this vertex. The number 11 you see is indeed the degree of vertex 5113. The Result type contains the method getLocalClusteringCoefficientScore() which allows you to retrieve the clustering coefficient score for a vertex. The method simply divides the numbers of triangles by the number of potential edges between neighbors.

I'm sorry that you this is not clear in the docs. We should definitely improve them to explain what is the output and how to retrieve the actual clustering coefficient values. I have opened a JIRA for this [1].

Cheers,

-Vasia.

[1]: https://issues.apache.org/jira/browse/FLINK-5597

On 20 January 2017 at 19:31, Miguel Coimbra <[hidden email]> wrote:

Hello,

In the documentation of the LocalClusteringCoefficient algorithm, it is said:

The local clustering coefficient measures the connectedness of each vertex’s neighborhood.
Scores range from 0.0 (no edges between neighbors) to 1.0 (neighborhood is a clique).

https://ci.apache.org/projects/flink/flink-docs-release-1.1/apis/batch/libs/gelly.html#local-clustering-coefficient

However, upon running the algorithm (undirected version), I obtained values above 1.

The result I got was this. As you can see, vertex 5113 has a score of 11:
(the input edges for the graph are shown further below - around 35 edges):

(4907,(1,0))
(5113,(11,0))
(6008,(0,0))
(6064,(1,0))
(6065,(1,0))
(6107,(0,0))
(6192,(0,0))
(6252,(1,0))
(6279,(1,0))
(6465,(1,0))
(6545,(0,0))
(6707,(1,0))
(6715,(1,0))
(6774,(0,0))
(7088,(0,0))
(7089,(1,0))
(7171,(0,0))
(7172,(1,0))
(7763,(0,0))
(7976,(1,0))
(8056,(1,0))
(9748,(1,0))
(10191,(1,0))
(10370,(1,0))
(10371,(1,0))
(14310,(1,0))
(16785,(1,0))
(19801,(1,0))
(26284,(1,0))
(26562,(0,0))
(31724,(1,0))
(32443,(1,0))
(32938,(0,0))
(33855,(1,0))
(37929,(0,0))

This was from a small isolated test with these edges:

5113    6008
5113    6774
5113    32938
5113    6545
5113    7088
5113    37929
5113    26562
5113    6107
5113    7171
5113    6192
5113    7763
9748    5113
10191    5113
6064    5113
6065    5113
6279    5113
4907    5113
6465    5113
6707    5113
7089    5113
7172    5113
14310    5113
6252    5113
33855    5113
7976    5113
<a href="tel:262%20845%20113" value="[phone]" target="_blank">26284    5113
8056    5113
10371    5113
16785    5113
19801    5113
6715    5113
31724    5113
32443    5113
10370    5113

I am not sure what I may be doing wrong, but is there perhaps some form of normalization lacking in my execution of:

org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient.Result;
org.apache.flink.graph.library.clustering.undirected.LocalClusteringCoefficient;

Am I supposed to divide all scores by the greatest score obtained by the algorithm?

Thank you very much!