Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation

With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user–item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.

Crucial Strategies to Solve Communicative Breakdown

English has increasingly become international language for business and commerce, science and technology, international relations and diplomacy. Due to this fact, the purpose of learning a foreign language is communication. Through communication, people send and receive messages and negotiate meaning. Communication has different forms and takes place in different situations. People communicate to satisfy their needs. Heterogeneous interaction is carried out by a native speaker and a non-native one in the purpose of exchanging of ideas, information between two or more individuals. There is usually, at least one speaker or sender, a message which transmitted, and an individual or individuals for whom this message is intended. Communication breakdowns may happen to anybody communicating in a language other than their dominating language. This problem, surely, can be solved but how? The primary aim of this article is to investigate the heterogeneous communication process in the terms of possible breakdown which happens to all people while communicating, so that they are unable to get their messages across express what they mean and what they understand. The author highlights crucial strategies toward solving these disruptions.

Reconstructing heterogeneous pathogen interactions from co-occurrence data via statistical network inference

Infectious diseases often involve multiple pathogen species or multiple strains of the same pathogen. As such, knowledge of how different pathogen species or pathogen strains interact is key to understand and predict the outcome of interventions that target only a single pathogen or subset of strains involved in disease. While population-level data have been used to infer pathogen strain interactions, most previously used inference methods only consider uniform interactions between all strains, or focus on marginal interactions between pairs of strains (without correction for indirect interactions through other strains). Here, we evaluate whether statistical network inference could be useful for reconstructing heterogeneous interaction networks from cross-sectional surveys tracking co-occurrence of multi-strain pathogens. To this end, we applied a suite of network models to data simulating endemic infection states of pathogen strains. Satisfactory performance was demonstrated by unbiased estimation of interaction parameters for large sample size. Accurate reconstruction of networks may require regularization or penalizing for sample size. Of note, performance deteriorated in the presence of host heterogeneity, but this could be overcome by correcting for individual-level risk factors. Our work demonstrates how statistical network inference could prove useful for detecting pathogen interactions and may have implications beyond epidemiology.

TECHNOLOGY MODELING INTEGRATION PROCESSES TO INCREASE THE LEVEL ROAD SAFETY VEHICLES

The article is devoted to the development of technologies for modeling integration processes that synergistically affect the level of road safety of vehicles in terms of risk and uncertainty of non-stationary environmental factors. The mathematical description of the given sphere and information space of interaction of road users in the zones of the greatest probability of commission of road accidents is formalized that is shown by statistics of supervision. The essence, peculiarity and specificity of situational modes of dynamic, continuous interaction between road users, drivers and environmental factors in the space-time discrete cells of the electronic map of the critical zone on the basis of the frequency of previous accidents are substantiated. Analytical means of formation of transversal trajectories of safe movement without accidents and catastrophes are offered, which are guaranteed by the corresponding on-board information controlled complexes (BICC) of vehicles. Active targeted management within the system of navigation and control of mobile objects is implemented with a bias on the criteria to increase the level of road safety directly in the current critical area of the road network. A method of discrete dynamic programming of processes of synthesis and realization of controlled motion on a guaranteed safe transverse trajectory has been developed, which is locally applied only situationally within the transient mode of evasion from approach to threats with shock contacts. The technology of parameterization of predicted mutual maneuvering functions in the conditions of individual restrictions on the local space-time zone of own motion for a pair of vehicles that are really close to each other is formalized. BICC information and analytical tools ensure the reliability of estimates of the interval of endurance according to the prejudiced piecewise continuous transverse curve, which also provides local points of avoidance of collisions or the passage of forbidden local microzones. The only methods of modeling different vehicles by each BICC actively synthesize paired microphase spatial divisions of their own trajectories, which, due to the presence of risks, record the absence of traffic accidents with loss of safety within nonstationary flows due to the coordinated multiple heterogeneous interaction under similar conditions of field approximations. KEY WORDS: UNPRECEDENTED SAFETY, TRAFFIC, GUARANTEED MANAGEMENT, COLLISION PREVENTION, REASONABLE DYNAMICS.

idenPC-CAP: Identify protein complexes from weighted RNA-protein heterogeneous interaction networks using co-assemble partner relation

Protein complexes play important roles in most cellular processes. The available genome-wide protein–protein interaction (PPI) data make it possible for computational methods identifying protein complexes from PPI networks. However, PPI datasets usually contain a large ratio of false positive noise. Moreover, different types of biomolecules in a living cell cooperate to form a union interaction network. Because previous computational methods focus only on PPIs ignoring other types of biomolecule interactions, their predicted protein complexes often contain many false positive proteins. In this study, we develop a novel computational method idenPC-CAP to identify protein complexes from the RNA-protein heterogeneous interaction network consisting of RNA–RNA interactions, RNA-protein interactions and PPIs. By considering interactions among proteins and RNAs, the new method reduces the ratio of false positive proteins in predicted protein complexes. The experimental results demonstrate that idenPC-CAP outperforms the other state-of-the-art methods in this field.

A multi-agent model to study epidemic spreading and vaccination strategies in an urban-like environment

Worldwide urbanization calls for a deeper understanding of epidemic spreading within urban environments. Here, we tackle this problem through an agent-based model, in which agents move in a two-dimensional physical space and interact according to proximity criteria. The planar space comprises several locations, which represent bounded regions of the urban space. Based on empirical evidence, we consider locations of different density and place them in a core-periphery structure, with higher density in the central areas and lower density in the peripheral ones. Each agent is assigned to a base location, which represents where their home is. Through analytical tools and numerical techniques, we study the formation mechanism of the network of contacts, which is characterized by the emergence of heterogeneous interaction patterns. We put forward an extensive simulation campaign to analyze the onset and evolution of contagious diseases spreading in the urban environment. Interestingly, we find that, in the presence of a core-periphery structure, the diffusion of the disease is not affected by the time agents spend inside their base location before leaving it, but it is influenced by their motion outside their base location: a strong tendency to return to the base location favors the spreading of the disease. A simplified one-dimensional version of the model is examined to gain analytical insight into the spreading process and support our numerical findings. Finally, we investigate the effectiveness of vaccination campaigns, supporting the intuition that vaccination in central and dense areas should be prioritized.