We model the emergence and propagation of reputations in social networks with a novel distributed algorithm. In social networks, reputations of agents (nodes) are emerged and propagated through interactions among the agents and through intrinsic and extrinsic consensus (voting) among neighbors influenced by the network topology. Our algorithm considers the degree information of nodes and of their neighbors to combine consensus in order to model how reputations travel within the network. In our algorithm, each node updates reputations on its neighbors by considering past interactions, computing the velocity of the interactions to measure how frequent the interactions have been occurring recently, and adjusting the feedback values according to the velocity of the interaction. The algorithm also captures the phenomena of accuracy of reputations decaying over time if interactions have not occurred recently. We present two contributions through experiments: (1) We show that an agent's reputation value is influenced by the position of the agent in the network and the neighboring topology; (2) We also show that our algorithm can compute more accurate reputations than existing algorithms especially when the topological information matters. The experiments are conducted in random social networks and Autonomous Systems Networks to find malicious nodes.