Emotions affect how we think and perceive information. Motivated by this, we present a novel framework for modeling emotions, and study its impact in social networks. We represent emotions on a circle by utilizing the circumplex model. These emotions spread in a group through the process of emotional contagion, and influence the interpersonal relationships therein. We propose a model to explain the process of emotional contagion, and its effect on the decision-making process, using a Laplacian-based flow. Our key finding in this direction shows that agents with similar emotions reach agreement eventually, exhibiting emotional contagion. Through this, the widely observed behaviors of consensus, polarization, and clustering of opinions follow in complex-weighted networks. Our approach relies on the equitable partitions and spectral properties of Laplacians in complex-weighted networks. Lastly, we validate our findings using synthetic and real-world datasets.
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