Social matching in complex social systems - Poh, Cleo (2018)
This project aims to create an Erdős–Rényi random network using the Stanford Network Analysis Platform(SNAP) and perform greedy matching algorithm with it to simulate social matching. Greedy matching algorithm makes nodes of the network prefer their neighbouring node with the highest fitness level. Nodes are paired up if they request each other, and they are removed from the network after being paired. The simulation results are used to evaluate the usefulness of such an algorithm to pair up individuals in a social network. The nodal degree distribution of the network is determined and displayed in a line graph in order to assess the validity of the network created. Results indicate that greedy matching algorithm is capable of pairing up nodes within 11% fitness level of the network and has a consistent efficiency (at least 10 loops of the algorithm to achieve maximum number of pairs) across different sizes of the network. The results are also compared to real-life scenarios, more specifically, online dating applications, to check if such an algorithm is applicable in real-life. The report concludes that greedy matching is suitable to be used as a foundation in social matching, but will require additional details (for example, restrictions to gender and age) to suit real-life needs.