Community detection is a research area from network science dealing withthe investigation of complex networks such as social or biological networks, aimingto identify subgroups (communities) of entities (nodes) thatare more closely relatedto each other inside the community than with the remaining entities in the network.Various community detection algorithms have been developed and used in the literaturehowever evaluating community structures that have been automatically detected isa challenging task due to varying results in different scenarios.Current evaluationmeasures that compare extracted community structures with the reference structure orground truth suffer from various drawbacks; some of them having beenpoint out in theliterature. Information theoretic measures form a fundamental classin this domain andhave recently received increasing interest. However even the well employed measures(NVI and NID) also share some limitations, particularly they are biased toward thenumber of communities in the network. The main contribution ofthis paper is tointroduce a new measure that overcomes this limitation while holding the importantproperties of measures. We review the mathematical properties of our measure based on¿2divergence inspired fromf-divergence measures in information theory. Theoreticalproperties as well as experimental results in various scenarios show the superiority of theproposed measure to evaluate community detection over the ones from the literature.