Dynamics of social contagions with local trend imitation

Research on social contagion dynamics has not yet including a theoretical analysis of the ubiquitous local trend imitation (LTI) characteristic. We propose a social contagion model with a tent-like adoption probability distribution to investigate the effect of this LTI characteristic on behavior spreading. We also propose a generalized edge-based compartmental theory to describe the proposed model. Through extensive numerical simulations and theoretical analyses, we find a crossover in the phase transition: when the LTI capacity is strong, the growth of the final behavior adoption size exhibits a second-order phase transition. When the LTI capacity is weak, we see a first-order phase transition. For a given behavioral information transmission probability, there is an optimal LTI capacity that maximizes the final behavior adoption size. Finally we find that the above phenomena are not qualitatively affected by the heterogeneous degree distribution. Our suggested theory agrees with the simulation results.Comment: 14 pages, 5 figure

Link Prediction in Complex Network via Penalizing Noncontribution Relations of Endpoints

Similarity based link prediction algorithms become the focus in complex network research. Although endpoint degree as source of influence diffusion plays an important role in link prediction, some noncontribution links, also called noncontribution relations, involved in the endpoint degree serve nothing to the similarity between the two nonadjacent endpoints. In this paper, we propose a novel link prediction algorithm to penalize those endpoints’ degrees including many null links in influence diffusion, namely, noncontribution relations penalization algorithm, briefly called NRP. Seven mainstream baselines are introduced for comparison on nine benchmark datasets, and numerical analysis shows great improvement of accuracy performance, measured by the Area Under roc Curve (AUC). At last, we simply discuss the complexity of our algorithm

Information filtering based on corrected redundancy-eliminating mass diffusion

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects’ attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets— Movilens, Netflix and Amazon—show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices

Behavioral Propagation Based on Passionate Psychology on Single Networks with Limited Contact

Passionate psychology behavior is a common behavior in everyday society but has been rarely studied on complex networks; so, it needs to be explored in more scenarios. In fact, the limited contact feature network will be closer to the real scene. In this paper, we study the influence of sensitive behavior and the heterogeneity of individual contact ability in a single-layer limited-contact network, and propose a single-layer model with limited contact that includes passionate psychology behaviors. Then, a generalized edge partition theory is used to study the information propagation mechanism of the model. Experimental results show that a cross-phase transition occurs. In this model, when individuals display positive passionate psychology behaviors, the final spreading scope will show a second-order continuous increase. When the individual exhibits negative sensitive behavior, the final spreading scope will show a first-order discontinuous increase In addition, heterogeneity in individuals’ limited contact capabilities alters the speed of information propagation and the pattern of global adoption. Eventually, the outcomes of the theoretic analysis match those of the simulations

Behavioral Propagation Based on Passionate Psychology on Single Networks with Limited Contact

Passionate psychology behavior is a common behavior in everyday society but has been rarely studied on complex networks; so, it needs to be explored in more scenarios. In fact, the limited contact feature network will be closer to the real scene. In this paper, we study the influence of sensitive behavior and the heterogeneity of individual contact ability in a single-layer limited-contact network, and propose a single-layer model with limited contact that includes passionate psychology behaviors. Then, a generalized edge partition theory is used to study the information propagation mechanism of the model. Experimental results show that a cross-phase transition occurs. In this model, when individuals display positive passionate psychology behaviors, the final spreading scope will show a second-order continuous increase. When the individual exhibits negative sensitive behavior, the final spreading scope will show a first-order discontinuous increase In addition, heterogeneity in individuals’ limited contact capabilities alters the speed of information propagation and the pattern of global adoption. Eventually, the outcomes of the theoretic analysis match those of the simulations

Personalized Recommendation via Suppressing Excessive Diffusion

Efficient recommendation algorithms are fundamental to solve the problem of information overload in modern society. In physical dynamics, mass diffusion is a powerful tool to alleviate the long-standing problems of recommendation systems. However, popularity bias and redundant similarity have not been adequately studied in the literature, which are essentially caused by excessive diffusion and will lead to similarity estimation deviation and recommendation performance degradation. In this paper, we penalize the popular objects by appropriately dividing the popularity of objects and then leverage the second-order similarity to suppress excessive diffusion. Evaluation on three real benchmark datasets (MovieLens, Amazon, and RYM) by 10-fold cross-validation demonstrates that our method outperforms the mainstream baselines in accuracy, diversity, and novelty