# Ranking and Cooperation in Real-World Complex Networks

## TL;DR

This paper explores how ranking algorithms and game theory models like the prisoner dilemma can predict cooperation levels among individuals in online social networks, aiding in identifying potential experts and their willingness to collaborate.

## Contribution

It introduces a combined approach using classical ranking algorithms and the prisoner dilemma to analyze cooperation and defection in complex networks.

## Key findings

- Correlations between node rank and cooperativity were identified.
- Neighborhood-based strategies provide insights into local cooperation patterns.
- Ranking values can predict individuals' propensity to cooperate.

## Abstract

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.