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XiaoSen Lee updated SPARK-17383: -------------------------------- External issue URL: https://github.com/apache/spark/pull/14940 Description: 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. was: 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. > 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 > > 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 (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org