1, 3,12, In community-based methods, in order to detect communities, above-mentioned quantities are used to calculate of a quality function or performance metric of the community detection methods such as "intensity" in clique-based methods, 25,26 "modularity" in modularity optimization methods, 15, probability of the function of the shared community affiliations in the method so-called BIGCLAM, 13 and other similar alternatives. The weights of network edges may be values such as cumulative contact time, 22 cumulative intercontact time, 21 the number of contacts, 23 the intercontact time mean, 24 the contact time mean, rate of the intercontact time mean to contact time mean, the related social metrics, or the value of a probability function of their relative random variables or similar quantities. This would allow us to pre-compute the weights of the utility function and dynamically change them as a node's view of the network is modified, leading to a more efficient dissemination. Furthermore, we adapt an existing dissemination solution to dynamically adjust the weights of the utility function based on a node's context, and show through simulations that it behaves better than the static version. We show that nodes do indeed behave differently and have different views of the network, but that familiar nodes (i.e., that meet each other often for long periods of time) are alike in terms of behavior. Thus, in this paper we wish to lay the foundation for a dynamic data routing solution for opportunistic networks. The network might be split into sub-networks that behave differently from each other (for instance, a group of nodes from the network might have many contacts, whereas some nodes might spend hours without encountering other peers). However, since mobile networks are extremely varied in terms of node type and behavior, this approach might prove not to be optimal. Most of the existing solutions pre-compute the weights based on offline observations, and apply the same values regardless of a node's context. The utility function computes weighted sums of various parameters, such as node centrality, similarity, trust, etc. When two nodes in an opportunistic network meet, a utility function is generally employed to select the data that have to be exchanged between them, in order to maximize the chance of message delivery and to minimize congestion.
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