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Label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best.

We introduce a novel model for attack vulnerability of complex networks with a tunable attack information parameter. Based on the model, we study the attack vulnerability of complex networks based on local information. We employ the generating function formalism to derive the exact value of the critical removal fraction of nodes for the disintegration of networks and the size of the giant component based on local information. We show that hiding just a small fraction of nodes can prevent the breakdown of a network and that it is a cost-efficient strategy for enhancing the robustness of complex networks to hide the information of networks.

The efficiency of a routing strategy on complex networks can be reflected by two measurements, i.e. the system capacity and the average data packets travel time. In this paper, we propose a new routing strategy which is only based on local information of network topology. This strategy integrated the delivering capability and packets queue length of nodes for enhancing the efficiency of traffic on scale-free networks. The probability that a given node i with delivering capability C_i receives packets from its neighbors is proportional to (N_i+1)/C_i and N_i is the packets queue length of the node i. Simulation results show that there exists an optimal value by maximizing the networks delivering capability and minimizing the packet travel time. We simulated the strategy on BA network with different m (connectivity density) values and the results show that our strategy is more efficient than other local information-based routing strategies.

Distillation protocols enable generation of high quality entanglement even in the presence of noise. Existing protocols ignore the presence of local information in mixed states produced from some noise sources such as photon loss, amplitude damping or thermalization. We propose new protocols that exploit local information in mixed states. Our protocols converge to higher fidelities in fewer rounds, and when local information is significant one of our protocols consistently improves yields by 10 fold or more. We demonstrate that our protocols can be compacted into an entanglement-pumping scheme, allowing quantum computation in distributed systems with a few qubits per location.