By Longbing Cao
This ebook constitutes the completely refereed and revised chosen papers from the tenth foreign Workshop on brokers and information Mining Interactions, ADMI 2014, held in Paris, France, in may well 2014 as satellite tv for pc workshop of AAMAS 2014, the thirteenth foreign convention on independent brokers and Multiagent Systems.
The eleven papers provided have been rigorously reviewed and chosen from a number of submissions for inclusion during this quantity. They current present learn and engineering effects, in addition to strength demanding situations and clients encountered within the respective groups and the coupling among brokers and information mining.
Read or Download Agents and Data Mining Interaction: 10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers PDF
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Extra info for Agents and Data Mining Interaction: 10th International Workshop, ADMI 2014, Paris, France, May 5-9, 2014, Revised Selected Papers
The proﬁle is built manually using keyword vectors of the visited web pages. Classiﬁcation of the web pages consists in comparison of the vector created for the web page with each concepts vector using the cosine similarity measure. K-nearest-neighbors technique is used to ﬁnd the top matches. The customer proﬁle is represented as the total weight and number of pages (documents) associated with each concept in the ontology. Most of the modern requirements to the customer proﬁle properties mentioned in Sect.
Scalable inﬂuence estimation in continuous-time diﬀusion networks. In: Advances in Neural Information Processing Systems, pp. 3147–3155 (2013) 17. : Multiplexity-facilitated cascades in networks. Phys. Rev. E 85(4), 045102 (2012) 18. : Relationship classiﬁcation in large scale online social networks and its impact on information propagation. In: Proceeding of 30th IEEE International Conference on Computer Communications, pp. 2291–2299 (2011) 19. : Epidemic outbreaks in complex heterogeneous networks.
1) Modeling Temporal Propagation Dynamics in Multiplex Networks 29 The factor ia (t) represents the fraction of agents activated by neighbors at time t. The factor Λb (t) is the fraction of agents in G1 which become active due to cross-layers propagation from G2 . In a topological network, degrees of agents are diﬀerent. As prevalent state can be expressed by an average over the various degree sets , ia (t) is derived by ia (t) = k Pa (k)ika (t). (2) The factor Pa (k) is the fraction of agents with k-degree in G1 and ika (t) denotes the fraction of agents in k -degree set which are activated by linking neighbors in G1 at time t.