# Phase Transitions of the k-Majority Dynamics in a Biased Communication Model

Consider a graph where each of the n nodes is in one of two possible states. Herein, we analyze the synchronous k-Majority dynamics, where nodes sample k neighbors uniformly at random with replacement and adopt the majority state among the nodes in the sample (potential ties are broken uniformly at random). This class of dynamics generalizes other well-known dynamics, e.g., Voter and 3-Majority, which have been studied in the literature as distributed algorithms for consensus. We consider a biased communication model: whenever nodes sample a neighbor they see state σ with some probability p, regardless of the state of the sampled node, and its true state with probability 1-p. Differently from previous works where specific graph topologies are considered, our analysis only requires the graphs to be sufficiently dense, i.e., to have minimum degree ω(log n), without any further topological assumption. In this setting we prove two phase transition phenomena, both occurring with high probability, depending on the bias p and on the initial unbalance toward state σ. More in detail, we prove that for every k≥ 3 there exists a p_k^⋆ such that if p>p_k^⋆ the process reaches in O(1) rounds a σ-almost-consensus, i.e., a configuration where a fraction 1-o(1) of the volume is in state σ. On the other hand, if p<p_k^⋆, we look at random initial configurations in which every node is in state σ with probability 1-q independently of the others. We prove that there exists a constant q_p,k^⋆ such that if q < q_p,k^⋆ then a σ-almost-consensus is still reached in O(1) rounds, while, if q > q_p,k^⋆, the process spends n^ω(1) rounds in a metastable phase where the fraction of volume in state σ is around a constant value depending only on p and k.

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