[ https://issues.apache.org/jira/browse/SPARK-17383?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-17383. ------------------------------- Resolution: Won't Fix > improvement LabelPropagation of graphx lib > ------------------------------------------ > > Key: SPARK-17383 > URL: https://issues.apache.org/jira/browse/SPARK-17383 > Project: Spark > Issue Type: Improvement > Components: GraphX > Affects Versions: 2.1.0 > Reporter: XiaoSen Lee > Priority: Major > > 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. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org