Agents and Data Mining Interaction: 10th International by Longbing Cao
By Longbing Cao
This publication constitutes the completely refereed and revised chosen papers from the tenth overseas Workshop on brokers and information Mining Interactions, ADMI 2014, held in Paris, France, in may possibly 2014 as satellite tv for pc workshop of AAMAS 2014, the thirteenth foreign convention on self sustaining brokers and Multiagent Systems.
The eleven papers awarded have been rigorously reviewed and chosen from a number of submissions for inclusion during this quantity. They current present study and engineering effects, in addition to power demanding situations and customers encountered within the respective groups and the coupling among brokers and information mining.
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Additional info for Agents and Data Mining Interaction: 10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers
1) to (7), the temporal dynamics of propagation in one layer of multiplex networks is described approximately. The expected fraction of active agents σa (t) = t ia (t) in G1 can be calculated by iterations of previous equations if related parameters are given. Meanwhile, diﬀusion process in G2 can also be obtained by exchanging the subscripts of two conjoint networks in above equations. The numerical modeling neglects the connectivity correlations in conjoint layers and the probability of pointing to an active agent is assumed to be independent of the connectivity of the agent .
The activations of a2 and b2 still follow the shortest propagation paths. Thus, cross-layers delay ﬁrst postpones the activation of conjoint agent and can further change the propagation process in conjoint layers. In real world, diverse individual biases on the types of information  may aﬀect the cross-layers delay. For instance, girls may frequently talk with friends about the new fashionable clothing posted by the social media while boys seldom do it in real life even if they have noticed the advertisements.
The factor Ψa (t) means the fraction of conjoint agents which are activated by neighbors in each layer of multiplex network at time t. As the mean-ﬁeld assumption indicates the uniform distribution of active agents in networks, Ψa (t) can be given by Ψa (t) = [ t ia (t)][ t ib (t)] − [ t−1 ia (t − 1)][ t−1 ib (t − 1)]. (7) 30 Z. Li et al. From Eqs. (1) to (7), the temporal dynamics of propagation in one layer of multiplex networks is described approximately. The expected fraction of active agents σa (t) = t ia (t) in G1 can be calculated by iterations of previous equations if related parameters are given.