Github user bookling commented on the issue:
https://github.com/apache/spark/pull/14880
In the labelPropagation of graphx lib, node is initialized with a unique
label and at every step each node adopts the label that most of its
neighbors currently have, but ignore the label it currently have. I think it is
unreasonable, because the labe a node had is also useful. When a node trend to
has a stable label, this means there is an association between two iterations,
so a node not only affected by its neighbors, but also its current label.
so I change the code, and use both the label of its neighbors and
itself.
This iterative process densely connected groups of nodes form a consensus
on a unique label to form
communities. But the communities of the LabelPropagation often
discontinuous.
Because when the label that most of its neighbors currents have are
many,e.g, node "0" has 6 neigbors labed {"1","1","2","2","3","3"},it maybe
randomly select a label. in order to get a stable label of communities, and
prevent the randomness, so I chose the max lable of node.
you can test graph with Edges:
{10L->11L,11L->12L,11L->14L,12L->14L,13L->14L,13L->15L,13L->16L,15L->16L,15L->17L,16L->17L
};or dandelion shape {1L->2L,2L->7L,2L->3L,2L->4L,2L->5L,2L->6L},etc.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
