The content discusses link prediction in social network analysis. It introduces four methods to compute the distance between two nodes in a social network and make predictions about missing links based on these distances. The four methods are: 

1. Common Neighbors: This method calculates the number of common neighbors between two nodes and normalizes it by the total number of nodes.
2. Jaccard's Coefficient: This method is a slight modification of the common neighbors method.
3. Adamic/Adar: This method calculates the sum of inverse log degree over all the common neighbors.
4. Katz<sub>β</sub>: This method computes a weighted sum of the number of paths of a specified length connecting two nodes.

The formulas for each method are provided in the content.
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SET SCHEMA DM_PAL;

DROP TABLE PAL_LINK_PREDICTION_DATA_TBL;
CREATE COLUMN TABLE PAL_LINK_PREDICTION_DATA_TBL(
	"NODE1" INTEGER, 
	"NODE2" INTEGER
);
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('1', '2');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('1', '4');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('2', '3');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('3', '4');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('5', '1');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('6', '2');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('7', '4');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('7', '5');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('6', '7');
INSERT INTO PAL_LINK_PREDICTION_DATA_TBL VALUES ('5', '4');

DROP TABLE #PAL_PARAMETER_TBL;
CREATE LOCAL TEMPORARY COLUMN TABLE #PAL_PARAMETER_TBL(
	"PARAM_NAME" NVARCHAR(256), 
	"INT_VALUE" INTEGER, 
	"DOUBLE_VALUE" DOUBLE, 
	"STRING_VALUE" NVARCHAR(1000)
);
INSERT INTO #PAL_PARAMETER_TBL VALUES ('METHOD', 1, NULL, NULL);

CALL _SYS_AFL.PAL_LINK_PREDICT(PAL_LINK_PREDICTION_DATA_TBL, "#PAL_PARAMETER_TBL", ?);

