{"push_id":1280711780,"size":1,"distinct_size":1,"ref":"refs/heads/master","head":"60e6f0ee2a3cdfb2b526a6d12887513f3aabed42","before":"bb875fef8f47ec99878d972f2c17b50123375a4c","commits":[{"sha":"60e6f0ee2a3cdfb2b526a6d12887513f3aabed42","author":{"name":"XiaoSen Lee","email":"0258bef35ddd45157eecdec321c54c847ec19211@sina.com"},"message":"Improvement labelPropagation of garphx lib\n\n\r\n\r\nIn the labelPropagation of graphx lib, node is initialized with a unique\r\nlabel 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.\r\nso I change the code, and use both the label of its neighbors and itself.\r\n\r\nThis iterative process densely connected groups of nodes form a consensus on a unique label to form\r\ncommunities. But the communities of the LabelPropagation often discontinuous.\r\nBecause 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.\r\n\r\nyou can test graph with Edges: {10L->11L,10L->12L, 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.","distinct":true,"url":"https://api.github.com/repos/bookling/spark/commits/60e6f0ee2a3cdfb2b526a6d12887513f3aabed42"}]}
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