By My T. Thai, Nam P. Nguyen, Huawei Shen
This booklet constitutes the refereed court cases of the 4th overseas convention on Computational Social Networks, CSoNet 2015, held in Beijing, China, in August 2015.
The 23 revised complete papers and three brief papers provided including 2 prolonged abstracts have been conscientiously reviewed and chosen from a hundred and one submissions and canopy themes on social details diffusion; community clustering and group constitution; social hyperlink prediction and advice; and social community constitution analysis.
Read Online or Download Computational Social Networks: 4th International Conference, CSoNet 2015, Beijing, China, August 4-6, 2015, Proceedings PDF
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Extra info for Computational Social Networks: 4th International Conference, CSoNet 2015, Beijing, China, August 4-6, 2015, Proceedings
Note that a consumer in Q who does not purchase at current time t under price pt may be willing to buy at a later time t > t under a lower price pt < pt . For each t = 1, 2, . . , at time t, or in the t-th round). We use r(p) to denote τ the revenue derived from p = (p1 , p2 , . . , r(p) = t=1 pt · |B(pt )|. In case of p = (p1 ), we often write r(p) as r(p1 ). The PNC problem is to ﬁnd a pricing sequence p = (p1 , p2 , . . , pτ ) such that r(p) is maximized, where both the length τ and the entries p1 , p2 , .
In the context of our problem of interest, 28 D. Kim et al. the ﬁrst version, namely MAXSAVE, aims to ﬁnd a valid vaccine strategy over time to maximize the number of uninfected after a given period. The second version, MINBUDGET, attempts to ﬁnd a valid vaccine strategy to save the members in a given node subset with a given graph such that the budget for the vaccines (the number of nodes removed) used this purpose is minimized. At a glance, MAXSAVE is similar to our problem of interest, SVDP2 .
Our paper is close to  in the sense that both papers address strategic substitutes (each player has less incentive to buy when more neighbors purchase), although the network externalities are negative in our settings but positive in their settings of public goods. In the computer science, the public goods pricing problem was also studied by Feldman et al. . Their work diﬀers from ours in two main respects: (i) In our externality model, a consumer’s utility is subtractive over the purchases made by this neighbors, whereas in their setting, purchases of neighbors are substitutes.