A Large Graph Sampling Visualization Method Based on Graph Representation Learning
A chart and representation technology, applied in still image data browsing/visualization, still image data retrieval, special data processing applications, etc., can solve problems such as not considering network semantic structure association, difficult connection of sampling results, uncertainty, etc., to achieve Maintain the characteristics of the network structure, simplify the context structure, and achieve the effect of reducing the scale
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[0018] Below in conjunction with accompanying drawing, the present invention will be further elaborated.
[0019] like figure 1 It is a flow chart of the large image sampling visualization method of the present invention, which specifically includes the following steps:
[0020] Step 1): Build a corpus. First, simulate a random walk sequence of fixed length L from a given source node u, w i Indicates the i-th node in the sequence, w i-1 Represents the i-1th node in the sequence. from w i = start from u, node w i Generated as shown in formula (1):
[0021]
[0022] That is, if there is an edge (v, x) in the network graph E, then the probability Select the next node x. Among them, π vx is the unregularized transition probability from node v to x, and Z is the regularization constant.
[0023] Then, based on the idea of 2nd-order random walks, let π vx = α pq (t,x), as shown in formula (2):
[0024]
[0025] Among them, d tx Indicates the distance of the sho...
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