By Hien T. Nguyen, Vaclav Snasel
This publication constitutes the refereed complaints of the fifth foreign convention on Computational Social Networks, CSoNet 2016, held in Ho Chi Minh urban, Vietnam, in August 2016.
The 30 revised complete papers offered have been conscientiously reviewed and chosen from seventy nine submissions. The papers hide issues on universal ideas, algorithms and instruments that govern social community structures/topologies, functionalities, social interactions, defense and privateness, community behaviors, details diffusions and effect, social advice structures that are appropriate to every kind of social networks and social media.
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Additional resources for Computational Social Networks: 5th International Conference, CSoNet 2016, Ho Chi Minh City, Vietnam, August 2-4, 2016, Proceedings
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F (v) = I{d(v) > 50} and (b). f (v) = c(v) (see (6)) used to estimate the average clustering coefficient. The plot in Fig. 3b shows the results for Friendster graph n . with super-node size 1000. Here the sequence a(n) is taken as 1/ 25 Now we concentrate on single sample path properties of the algorithms. Hence the numerator of NRMSE becomes absolute error. Figure 3c shows the effect of increasing super-node size while fixing step size a(n) and Fig. 3d shows the effect of changing a(n) when super-node is fixed.
Hence the distribution of √N (N ) N (ˆ μRDS (G) − μ(G)) is the same as that of N t=1 f (Xt ) N t=1 g(Xt ) D = (N ) √1 z + μf N 1 (N ) √1 z + μg N 2 √ √ (N ) N μf z1 + N μf = (N ) √ =√ (N ) z2 z 2 + N μg N μg (1 + √N ) μ (N ) z1 + g (N ) (1)z2 1 z (N ) =√ (z1 − √ N μg N μg (N ) + √ N μf − (N ) z2 μf μg 1 + O( √ )) N 32 K. Avrachenkov et al. This gives √ N N t=1 f (Xt ) N t=1 g(Xt ) − μf μg D −→ 1 μg z1 − z2 μf μg , since the term O( √1N ) tend to zero in probability, and using Slutsky’s lemma [3].
7(a) and (b), we infer that when new nodes (ΔH 2 ) join H21 , they create more connections with the nodes already in the system. Figure 7(c) shows the degree distribution for edges of category 3. We observe that the shape of the degree distribution is decreasing which implies that the network is become more dense (densification), as discussed above. 24 B. Dumba et al. (a) Category 1 (b) Category 2 (c) Category 3 Fig. 7. Degree distribution per edge category (Color figure online) In summary, our analysis on the categories of nodes and edges, in this section, led to the following key findings: (a) the majority of users that joins the reciprocal network of G+ are new users in the network and they tend to create reciprocal connections mostly to users who already have reciprocal connections to others; (b) if a user does not create a reciprocal edge when he/she joins G+, there is a lower chance that he/she will create one later.