5. Results and discussion

FUNCTION mjr(use,links)

FUNCTION level2(use,links)

Set cumulativeWeight = 0; FOREACH links as link FOREACH payloads as payload

return cumulativeWeight;

FUNCTION mnr(use,links)

Set cumulativeWeight = 0; FOREACH links as link FOREACH payloads as payload

return cumulativeWeight;

ELSE return 0; END IF END FUNCTION

END IF END FOREACH END FOREACH

END IF END FOREACH END FOREACH

END FUNCTION

neutral or zero (0).

4.3. Acceptance threshold

END FUNCTION

Set payloads = use.get['payload']; return payloads['majorWeight'];

14 Recent Progress in Parallel and Distributed Computing

Set payloads = use.get['payload']; Set payloads = payloads.get['level2'];

IF payload.get['domainID'] == link

Set payloads = use.get['payload']; Set payloads = payloads.get['minor'];

IF payload.get['domainID'] == link

Set cumulativeWeight += payload.get['weight'];

Threshold value represents the level of certainty both the source node and the recipient node must adhere to. By increasing the threshold value means increasing the quality of influencing messages and the level of trust propagated from the source social node, hence it potentially reducing the number of successfully influenced social nodes. Since there is no common specification on what threshold value should be used and the values often depend on the researcher's preference, threshold value used in this research is set to

Set cumulativeWeight += payload.get['weight'];

IF links.getLastElemet() == use.get['major'])

In this article, results generated from the proposed genetic algorithm diffusion model (GADM), enhanced genetic algorithm diffusion model (T-GADM) and domain specified trust-enhanced genetic algorithm diffusion model (DST-GADM) will be compared and discussed respectively. Figure 4 shows the difference between results generated from the base algorithm and the trust-enhanced algorithm on the effects of the rates of successfully influenced social nodes within the simulated social networking environment, whereas Figure 5 illustrates the successful influence acceptance rates with threshold value set to default (0).

Figure 4. Acceptance probability for GADM vs. T-GADM.

Figure 5. Acceptance statistics for GADM vs. T-GADM.

Figure 6. Acceptance probability for GADM vs. T-GADM vs. DST-GADM Tier 1, 2 and 3.

Figure 7. Acceptance rates for GADM vs. T-GADM vs. DST-GADM Tier 1, 2 and 3.

All percentages presented in this section are calculated by averaging the differences on the rate of successfully influenced social nodes between two or more algorithms where the GADM algorithm will serve as the benchmark. The analysis showed that by comparing results generated between GADM (base algorithm without trusted social node) and T-GADM (enhanced algorithm with trusted social node), the T-GADM algorithm yields 5.79% increment on the rate of successfully influenced social nodes compared to GADM. Such increment on the rate of successfully influenced social nodes is because social nodes that are trustworthy have higher tendency of being accepted by other social nodes; therefore, influence spread by these trustworthy social nodes may strongly be accepted. Furthering the analysis, results generated show that the DST-GADM (domain specified trust influence) yields a further 4% improvement on the rate of successfully influenced social nodes when it is compared to T-GADM (enhanced algorithm with trusted social node) and about 10% improvement on the rate of successfully influenced social nodes when it is compared to GADM (base algorithm without trusted social node). Such improvement is expected since it is said that trusted social nodes that share the similar interest would be better at influencing social nodes within the interest group. Analysis results are illustrated in Figures 6 and 7. Both results also concluded the hypothesis presented in this article where it is said that social trust plays an important role in influential propagation, and it is able to increase the rate of success in influencing other social nodes in a social network.
