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With so many pairs you have the possibility of investigating more profoundly the structure, like if the difference $y_B - y_A$ depends on the "starting point" $y_A$!

You could fit a model like $$ y_B=\mu+\text{offset}(y_A) +\Delta (y_A-\bar{y}_A) + \epsilon $$ and you could even add a quadratic term $+\Delta_2 (y_A-\bar{y}_A)^2$ or you could replace the linear+quadratic term with a spline using a generalized additive model (or regression splines).

Graphically you could show the lines as you have shown, with a reduced alpha factor (*), maybe reducing further by only showing a random sample of lines. Then you could color the lines according to slope ...

(*) alpha factor here is a graphical parameter making points in the plot transparent, so the first plotted points is not totally occulted by later overplotting.

With so many pairs you have the possibility of investigating more profoundly the structure, like if the difference $y_B - y_A$ depends on the "starting point" $y_A$!

You could fit a model like $$ y_B=\mu+\text{offset}(y_A) +\Delta (y_A-\bar{y}_A) + \epsilon $$ and you could even add a quadratic term $+\Delta_2 (y_A-\bar{y}_A)^2$ or you could replace the linear+quadratic term with a spline using a generalized additive model (or regression splines).

Graphically you could show the lines as you have shown, with a reduced alpha factor (*), maybe reducing further by only showing a random sample of lines. Then you could color the lines according to slope ...

For Bland-Altman plots, mentioned in a comment by Nick Cox, see for instance for an example Agreement between methods with multiple observations per individual or look through the tag bland-altman-plot.

(*) alpha factor here is a graphical parameter making points in the plot transparent, so the first plotted points is not totally occulted by later overplotting.

With so many pairs you have the possibility of investigating more profoundly the structure, like if the difference $y_B - y_A$ depends on the "starting point" $y_A$!

You could fit a model like $$ y_B=\mu+\text{offset}(y_A) +\Delta (y_A-\bar{y}_A) $$ and you could even add a quadratic term $+\Delta_2 (y_A-\bar{y}_A)^2$ or you could replace the linear+quadratic term with a spline using a generalized additive model.

Graphically you could show the lines as you have shown, with a reduced alpha factor (*), maybe reducing further by only showing a random sample of lines. Then you could color the lines according to slope ...

(*) alpha factor here is a graphical parameter making points in the plot transparent, so the first plotted points is not totally occulted by later overplotting.

With so many pairs you have the possibility of investigating more profoundly the structure, like if the difference $y_B - y_A$ depends on the "starting point" $y_A$!

You could fit a model like $$ y_B=\mu+\text{offset}(y_A) +\Delta (y_A-\bar{y}_A) + \epsilon $$ and you could even add a quadratic term $+\Delta_2 (y_A-\bar{y}_A)^2$ or you could replace the linear+quadratic term with a spline using a generalized additive model (or regression splines).

Graphically you could show the lines as you have shown, with a reduced alpha factor (*), maybe reducing further by only showing a random sample of lines. Then you could color the lines according to slope ...

(*) alpha factor here is a graphical parameter making points in the plot transparent, so the first plotted points is not totally occulted by later overplotting.

With so many pairs you have the possibility of investigating more profoundly the structure, like if the difference $y_B-y_A$ depends on the "starting point" $y_A$!

You could fit a model like $$ y_B=\mu+\text{offset}(y_A) +\Delta (y_A-\bar{y}_A) $$ and you could even add a quadratic term $+\Delta_2 (y_A-\bar{y}_A)^2$ or you could replace the linear+quadratic term with a spline using a generalized additive model.

Graphically you could show the lines as you have shown, with a reduced alpha, maybe reducing further by only showing a random sample of lines. Then you could color the lines according to slope ...

With so many pairs you have the possibility of investigating more profoundly the structure, like if the difference $y_B - y_A$ depends on the "starting point" $y_A$!

You could fit a model like $$ y_B=\mu+\text{offset}(y_A) +\Delta (y_A-\bar{y}_A) $$ and you could even add a quadratic term $+\Delta_2 (y_A-\bar{y}_A)^2$ or you could replace the linear+quadratic term with a spline using a generalized additive model.

Graphically you could show the lines as you have shown, with a reduced alpha factor (*), maybe reducing further by only showing a random sample of lines. Then you could color the lines according to slope ...

(*) alpha factor here is a graphical parameter making points in the plot transparent, so the first plotted points is not totally occulted by later overplotting.