  People participate and activate in online social networks and thus tremendous
amount of network data is generated; data regarding their interactions,
interests and activities. Some people search for specific questions through
online social platforms such as forums and they may receive a suitable response
via experts. To categorize people as experts and to evaluate their willingness
to cooperate, one can use ranking and cooperation problems from complex
networks. In this paper, we investigate classical ranking algorithms besides
the prisoner dilemma game to simulate cooperation and defection of agents. We
compute the correlation among the node rank and node cooperativity via three
strategies. The first strategy is involved in node level; however, other
strategies are calculated regarding neighborhood of nodes. We find out
correlations among specific ranking algorithms and cooperativtiy of nodes. Our
observations may be applied to estimate the propensity of people (experts) to
cooperate in future based on their ranking values.
