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No abstract received. [ABSTRACT FROM AUTHOR]

Community detection is an important issue to understand the structural and functional properties of complex networks, which still remains a challenging subject. In some complex networks, a node may belong to multiple communities, implying overlapping community structure. Moreover, complex networks often show a hierarchical structure where small communities group together to form larger ones. In this paper, we propose a novel parameter-free algorithm called agglomerative clustering based on label propagation algorithm (ACLPA) to detect both overlapping and hierarchical community structure in complex networks. By combining the advantages of agglomerative clustering and label propagation, our algorithm can build the hierarchical tree of overlapping communities in large-scale networks. The tests on both synthetic and real-world networks give excellent results and demonstrate the effectiveness and efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Coordinating multiagent systems to maximize global information collection is a key challenge in many real world applications such as planetary exploration, and search and rescue. In particular, in many domains where communication is expensive (e.g., in terms of energy), the coordination must be achieved in a passive manner, without agents explicitly informing other agents of their states and/or intended actions. In this work, we extend results on such multiagent coordination algorithms to domains where the agents cannot achieve the required tasks without forming teams. We investigate team formation in three types of domains, one where n agents need to perform a task for the team to receive credit, one where there is an optimal number of agents (n) required for the task, but where the agents receive a decaying reward if they form a team with membership other than n, and finally we investigate heterogeneous teams where individuals vary in construction. Our results show that encouraging agents to coordinate is more successful than strictly requiring coordination. We also show that devising agent objective functions that are aligned with the global objective and locally computable significantly outperform systems where agents directly use the global objective, and that the improvement increases with the complexity of the task. [ABSTRACT FROM AUTHOR]