Competitive Interaction Model for Online Social Networks’ Users’ Data Forwarding at a Subnet

Abstract

Social Networks Ecosystem is evolving faster and Social Networks users' intensive activities are affecting significantly the network traffic. Generated data from different sending and receiving hosts have to be handled by network active nodes and links. In this paper, we propose to study the interaction between two types of network active hosts by using a Lotka-Volterra competitive system that considers the subnet limiting supply. We applied our proposed model to two kinds of Online Social Networks' (OSNs') users characterized by the intensity of their activities from the generated data perspective. We assumed and proved that the impact of competition is flexible and subject to forwarding protocol and subnet allocated resources. By taking the competition case in the analysis of the differential equations, we show that when competition exists, one stable equilibrium point can be found if certain conditions are respected. Numerical results confirm our theoretical analysis and show that instead of treating equally at the subnet level all data from active hosts, adopting a routing protocol that takes into account the nature of data and the forwarding time is necessary for improving the overall performance of the network.

Unsupervised Negative Link Prediction in Signed Social Networks

“…In order to explore the impact of different input data on UN-PNMF, eight groups of experiments are designed to observe the results. Other parameters are fixed, and G t n can be {G 5 n , G 6 n , G 7 n , G 8 n }. This part integrates the different experimental results of different G t n , and the comparison results are shown in Figure 4 and Table 2.…”

“…It also finds that the prediction accuracy of short-term is much better than long-term. For example, when the input matrix is G 5 n , the accuracy of UN-PNMF predicting G 6 n is 16.71%, but the accuracy of UN-PNMF predicting G 10 n is 9.15% in Slashdot. The difference between them is 7.56%; hence the results of each group of experiments present descending trend in Figure 4.…”

“…Trust, which provides information about from whom users should accept information and with whom users should share information, plays an important role in helping online users collect reliable information [2]. Recent years have witnessed many trust-related online applications, such as trust-aware recommendation systems [5][6][7], high-quality user-generated content finding [8,9], and viral marketing [2]. All in all, by studying the positive links in signed network, people can find potentially valuable knowledge.…”

Asymptotic Behavior of a Predator-Prey Model with Allee Threshold Applied to Online Social Network Users’ Data Forwarding

“…Such models are widely spread and intensively studied in related literature. Particularly, models with Allee-threshold extinction control mechanism present the advantage of capturing such variations of species growth rate by setting a maximum value to reach for population size to not go extinct [10][11][12][13][14][15].…”

Dynamic of a Delayed Predator-Prey Model with Application to Network’ Users’ Data Forwarding

“…Particularly, one can find several modified Rosenzweig-MacArthur models studied in the related literature 3,6,14–20 . In our approach, instead of modelling the underlying relationship between network users using a classical predator-prey competitive system as in the previous published article 21 , we have chosen a Rosenzweig-MacArthur type model for its accuracy in capturing density dependency phenomenon and sensitivity to small perturbations. While a classical competitive model focuses mainly on the outcome of competition, this can be limiting for tuning or adjusting parameters.…”