I'll answer the second question first. The two are distinct concepts — there's no way directly compare graphs or results without more info or context.
Differential privacy is usually obtained by 1. computing a function $F$ of the data and 2. adding some noise to the result. The noise must be large enough to hide an individual contribution, and "how well an individual contribution is hidden" is captured by the parameter $\varepsilon$.
But for some functions $F$, an individual contribution can change the true result more than for others functions. For example:
- The answer to "how many employees of this company have blue eyes?" can only change by one if you add or remove an employee.
- The answer to "what's the average salary of people in this company?" can change by a lot if you remove the CEO's salary.
That concept is captured by sensitivity: the higher the possible change, the higher the sensitivity. And typically, to get $\varepsilon$-differential privacy with a fixed $\varepsilon$, you have to add more noise when when the sensitivity is larger (to compensate). That's the typical relationship between the two concepts.