Influential Neighbors Research Articles

Outward expansion or networked growth? Comparing farmland loss in evolving urban agglomerations across Mollisol regions in the northern hemisphere

Many cities in Mollisol (black earth) regions need to revitalize their local economies while avoiding excessive farmland consumption, which could have a global impact. To resolve this dilemma, we have to understand how urbanization interacts with farmland loss, which requires factoring in their interconnected influences. The Chicago urban agglomeration (CUA), Harbin−Changchun urban agglomeration (HCUA) and Dnipro−Kamianske urban agglomeration (DKUA) were selected to compare farmland loss in relation to urban morphology, growth patterns and networked cities during 2000–2020. Our results show that trends in farmland loss can be differentiated between individual cities, and also controlled via their interconnections. Overall, expansion of CUA and DKUA has decelerated but HCUA is still undergoing rapid urbanization, with its area increasing by 116 %. Urbanization pressure has shifted from core cities to small and medium-sized cities, which have also experienced directional farmland loss, towards their influential neighbors. Farmland loss disparities between different intra- and inter-UA subgroups are prominent, demonstrating that occupation pressure is diverse and phased in a UA, and cannot be addressed by a one-size-fits-all form of zoning control. These findings support the conservation and protection of fertile soils from urbanization pressure, and promote international cooperation between global Mollisol regions.

The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model.

In moving animal groups, social interactions play a key role in the ability of individuals to achieve coordinated motion. However, a large number of environmental and cognitive factors are able to modulate the expression of these interactions and the characteristics of the collective movements that result from these interactions. Here, we use a data-driven fish school model to quantitatively investigate the impact of perceptual and cognitive factors on coordination and collective swimming patterns. The model describes the interactions involved in the coordination of burst-and-coast swimming in groups of Hemigrammus rhodostomus. We perform a comprehensive investigation of the respective impacts of two interactions strategies between fish based on the selection of the most or the two most influential neighbors, of the range and intensity of social interactions, of the intensity of individual random behavioral fluctuations, and of the group size, on the ability of groups of fish to coordinate their movements. We find that fish are able to coordinate their movements when they interact with their most or two most influential neighbors, provided that a minimal level of attraction between fish exist to maintain group cohesion. A minimal level of alignment is also required to allow the formation of schooling and milling. However, increasing the strength of social interactions does not necessarily enhance group cohesion and coordination. When attraction and alignment strengths are too high, or when the heading random fluctuations are too large, schooling and milling can no longer be maintained and the school switches to a swarming phase. Increasing the interaction range between fish has a similar impact on collective dynamics as increasing the strengths of attraction and alignment. Finally, we find that coordination and schooling occurs for a wider range of attraction and alignment strength in small group sizes.

Three Birds With One Stone: User Intention Understanding and Influential Neighbor Disclosure for Injection Attack Detection

Recommender system, as a data-driven way to help customers locate products that match their interests, is increasingly critical for providing competitive customer suggestions in many web services. However, recommender systems are highly vulnerable to malicious injection attacks due to their fundamental vulnerabilities and openness. With the endless emergence of new attacks, how to provide a feasible way for defending different malicious threats against online recommendations is still an under-explored issue. In this paper, we explore a new way to defend malicious injection attacks through user intention understanding and influential neighbour disclosure. Specifically, we propose a detection approach, termed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> hree <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</u> irds with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</u> ne <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> tone), to deal with different malicious threats. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> , we first develop the discrimination of attack target by combining global influence evaluation and risk attitude estimation of users. In order to make <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> controllable, second, we propose to incorporate an optimal denoising mechanism to remove disturbed information before detection. To enhance the representativeness and predictability of detection model, finally, we propose to leverage a behavioral label propagation mechanism based on constructed label space for the determination of malicious injection behaviors. Extensive experiments on both synthetic and real data demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> outperforms all baselines in different cases. Particularly, the detection performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> can achieve an improvement of 6.08% FAR (false alarm rate) for optimal-injection attacks, an improvement of 3.83% FAR in average for co-visitation injection attacks, as well as an improvement of 2.3% for profile injection attacks over benchmarks in terms of FAR while keeping the highest DR (detection rate). Additional experiments on real-world data show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TBOS</i> brings an improvement with the advantage of 6.5% FAR in average compared with baselines.

Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection.

Young MSM (YMSM) bear a disproportionate burden of HIV infection in the United States and their risks of acquiring HIV may be shaped by complex multilayer social networks. These networks are formed through not only direct contact with social/sex partners but also indirect anonymous contacts encountered when attending social venues. We introduced a new application of a state-of-the-art graph-based deep learning method to predict HIV infection that can identify influential neighbors within these multiple network contexts. We used empirical network data among YMSM aged 16-29 years old collected from Houston and Chicago in the United States between 2014 and 2016. A computational framework GAT-HIV (Graph Attention Networks for HIV) was proposed to predict HIV infections by identifying influential neighbors within social networks. These networks were formed by multiple relations constituted of social/sex partners and shared venue attendances, and using individual-level variables. Further, GAT-HIV was extended to combine multiple social networks using multigraph GAT methods. A visualization tool was also developed to highlight influential network members for each individual within the multiple social networks. The multigraph GAT-HIV models obtained average AUC values of 0.776 and 0.824 for Chicago and Houston, respectively, performing better than empirical predictive models (e.g. AUCs of random forest: 0.758 and 0.798). GAT-HIV on single networks also delivered promising prediction performances. The proposed methods provide a comprehensive and interpretable framework for graph-based modeling that may inform effective HIV prevention intervention strategies among populations most vulnerable to HIV.

MINE: Identifying Top-k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion

Identifying vital nodes in complex networks is of paramount importance in understanding and controlling the spreading dynamics. Currently, this study is facing great challenges in dealing with big data in many real-life applications. With the deepening of the research, scholars began to realize that the analysis on traditional graph model is insufficient because many nodes in a multilayer network share connections among different layers. To address this problem both efficiently and effectively, a novel algorithm for identifying vital nodes in both monolayer and multilayer networks is proposed in this paper. Firstly, a node influence measure is employed to determine the initial leader of a local community. Subsequently, the community structures are revealed via the Maximum Influential Neighbors Expansion (MINE) strategy. Afterward, the communities are regarded as super-nodes for an iteratively folding process till convergence, in order to identify influencers hierarchically. Numerical experiments on 32 real-world datasets are conducted to verify the performance of the proposed algorithm, which shows superiority to the competitors. Furthermore, we apply the proposed algorithm in the graph of adjacencies derived from the maps of China and USA. The comparison and analysis of the identified provinces (or states) suggest that the proposed algorithm is feasible and reasonable on real-life applications.

Node influence-based label propagation algorithm for semi-supervised learning

Graph-based semi-supervised learning (GSSL) has received more and more attention due to its efficiency and accuracy. Label propagation is a critical step in GSSL that propagates label information to unlabeled data through the structure of graph. However, the traditional label propagation algorithms treat all unlabeled samples as equivalent and blindly propagate label information to all neighbors without considering their reliabilities. In this case, some unreliable samples may mislead the process of label propagation, thus greatly reducing the accuracy of classification. In order to solve this problem, this paper proposes a novel label propagation algorithm called node influence-based label propagation (NILP). Based on the structure of graph, the NILP algorithm measures the influences of nodes by calculating their degrees and local densities. In the process of label propagation, the label information is preferentially transmitted to the influential neighbors to control the propagation sequence and prevent wrong propagation. Moreover, our algorithm improves the transition matrix by integrating label information and feature information. The experimental results on both synthetic and real-world benchmark datasets show that the proposed method is superior to some existing label propagation algorithms. Especially when the number of labeled samples is very small, the advantage of NILP algorithm is more obvious.

Influential neighbours: Seeds of dominant species affect the germination of common grassland species

AbstractQuestionsGermination is a prerequisite of successful establishment in plant communities and is influenced by many factors. Therefore, seeds are under strong selective pressure to sense and integrate information about their environment and modulate germination based on it. In this study, we focus on interspecific seed–seed interactions under optimal and suboptimal conditions to test three hypotheses: (H1) dominant species’ seeds and emerging seedlings are most likely to be recognised by neighbouring seeds and exert a significant effect on the germination of common subordinate species; (H2) taxonomically related species are expected to exert stronger influence than unrelated species on the germination of neighbouring seeds; and (H3) facilitative interactions are more likely to occur under suboptimal conditions (drought stress) in the seed–seed interactions.LocationSemi‐dry temperate grassland belonging to the Festuco‐Brometea class, Cluj‐Napoca, Romania.MethodsWe assessed the rate and speed of germination of three common subordinate Asteraceae species (target species) in a controlled germination experiment. The target species’ seeds were sown in combination with low and high densities of neighbour species: two dominant Poaceae species, two subordinate Asteraceae species and two subordinate Apiaceae species; under optimal or drought conditions.ResultsUnder optimal water conditions, particularly the seeds of the two dominant Poaceae species affected the germination of target species. Under drought stress, almost all neighbouring species altered the germination of two of the target species, enhancing or reducing their germination rate. Facilitation in seed–seed interactions was proved to be species‐specific rather than general under drought conditions.ConclusionsWe found evidence that the status in a plant community (dominant or subordinate), but not the taxonomic relatedness influences the outcome of seed–seed interactions during germination. Under drought stress, the persistent competitive effect of the dominant species might considerably hinder the recruitment of subordinate grassland species.

Computational and robotic modeling reveal parsimonious combinations of interactions between individuals in schooling fish.

Coordinated motion and collective decision-making in fish schools result from complex interactions by which individuals integrate information about the behavior of their neighbors. However, little is known about how individuals integrate this information to take decisions and control their motion. Here, we combine experiments with computational and robotic approaches to investigate the impact of different strategies for a fish to interact with its neighbors on collective swimming in groups of rummy-nose tetra (Hemigrammus rhodostomus). By means of a data-based agent model describing the interactions between pairs of H. rhodostomus (Calovi et al., 2018), we show that the simple addition of the pairwise interactions with two neighbors quantitatively reproduces the collective behavior observed in groups of five fish. Increasing the number of interacting neighbors does not significantly improve the simulation results. Remarkably, and even without confinement, we find that groups remain cohesive and polarized when each agent interacts with only one of its neighbors: the one that has the strongest contribution to the heading variation of the focal agent, dubbed as the "most influential neighbor". However, group cohesion is lost when each agent only interacts with its nearest neighbor. We then investigate by means of a robotic platform the collective motion in groups of five robots. Our platform combines the implementation of the fish behavioral model and a control system to deal with real-world physical constraints. A better agreement with experimental results for fish is obtained for groups of robots only interacting with their most influential neighbor, than for robots interacting with one or even two nearest neighbors. Finally, we discuss the biological and cognitive relevance of the notion of "most influential neighbors". Overall, our results suggest that fish have to acquire only a minimal amount of information about their environment to coordinate their movements when swimming in groups.

Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization

Influence maximization aims to select a subset of top-k influential nodes to maximize the influence propagation, and it remains an open research topic of viral marketing and social network analysis. Submodularity-based methods including greedy algorithm can provide solutions with performance guarantee, but the time complexity is unbearable especially in large-scale networks. Meanwhile, conventional centrality-based measures cannot provide steady performance for multiple influential nodes identification. In this paper, we propose an improved discrete particle swarm optimization with an enhanced network topology-based strategy for influence maximization. According to the strategy, the k influential nodes in a temporary optimal seed set are recombined firstly in ascending order by degree metric to let the nodes with lower degree centrality exploit more influential neighbors preferentially. Secondly, a local greedy strategy is applied to replace the current node with the most influential node from the direct neighbor set of each node from the temporary seed set. The experimental results conducted in six social networks under independent cascade model show that the proposed algorithm outperforms typical centrality-based heuristics, and achieves comparable results to greedy algorithm but with less time complexity.

Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

Identifying influential spreaders in complex networks is crucial for containing virus spread, accelerating information diffusion, and promoting new products. In this paper, inspired by the effect of leaders on social ties, we propose the most influential neighbors’ k-shell index that is the weighted sum of the products between k-core values of itself and the node with the maximum k-shell values. We apply the classical Susceptible-Infected-Recovered (SIR) model to verify the performance of our method. The experimental results on both real and artificial networks show that the proposed method can quantify the node influence more accurately than degree centrality, betweenness centrality, closeness centrality, and k-shell decomposition method.

Lazy collaborative filtering with dynamic neighborhoods

PurposeThe purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information.Design/methodology/approachThe proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods.FindingsThe results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods.Research limitations/implicationsIn the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency.Originality/valueThe LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.

Correction: Identifying influential neighbors in animal flocking.

[This corrects the article DOI: 10.1371/journal.pcbi.1005822.].

Identifying influential neighbors in animal flocking.

Schools of fish and flocks of birds can move together in synchrony and decide on new directions of movement in a seamless way. This is possible because group members constantly share directional information with their neighbors. Although detecting the directionality of other group members is known to be important to maintain cohesion, it is not clear how many neighbors each individual can simultaneously track and pay attention to, and what the spatial distribution of these influential neighbors is. Here, we address these questions on shoals of Hemigrammus rhodostomus, a species of fish exhibiting strong schooling behavior. We adopt a data-driven analysis technique based on the study of short-term directional correlations to identify which neighbors have the strongest influence over the participation of an individual in a collective U-turn event. We find that fish mainly react to one or two neighbors at a time. Moreover, we find no correlation between the distance rank of a neighbor and its likelihood to be influential. We interpret our results in terms of fish allocating sequential and selective attention to their neighbors.

From susceptibility to frailty in social networks: The case of obesity

ABSTRACTThe obesity pandemic is represented by a discrete-time Hopfield Boolean network embedded in continuous-time population dynamics. The influence of the social environment passes through a system of differential equations, whereby obesity spreads by imitation of the most influential neighbors, those who have the highest centrality indices in the network. This property is called “homophily.” Susceptibility and frailty are redefined using network properties. Projections of the spread of obesity are validated on data collected in a French high school.

Genetic-based interactions among tree neighbors: identification of the most influential neighbors, and estimation of correlations among direct and indirect genetic effects for leaf disease and growth in Eucalyptus globulus.

An individual's genes may influence the phenotype of neighboring conspecifics. Such indirect genetic effects (IGEs) are important as they can affect the apparent total heritable variance in a population, and thus the response to selection. We studied these effects in a large, pedigreed population of Eucalyptus globulus using variance component analyses of Mycosphearella leaf disease, diameter growth at age 2 years, and post-infection diameter growth at ages 4 and 8 years. In a novel approach, we initially modeled IGEs using a factor analytic (FA) structure to identify the most influential neighbor positions, with the FA loadings being position-specific regressions on the IGEs. This involved sequentially comparing FA models for the variance-covariance matrices of the direct and indirect effects of each neighbor. We then modeled IGEs as a distance-based, combined effect of the most influential neighbors. This often increased the magnitude and significance of indirect genetic variance estimates relative to using all neighbors. The extension of a univariate IGEs model to bivariate analyses also provided insights into the genetic architecture of this population, revealing that: (1) IGEs arising from increased probability of neighbor infection were not associated with reduced growth of neighbors, despite adverse fitness effects being evident at the direct genetic level; and (2) the strong, genetic-based competitive interactions for growth, established early in stand development, were highly positively correlated over time. Our results highlight the complexities of genetic-based interactions at the multi-trait level due to (co)variances associated with IGEs, and the marked discrepancy occurring between direct and total heritable variances.

Challenges in China's health system reform: lessons from other countries.

We are very pleased to have been invited to edit the inaugural virtual special issue for the Australian Journal of Primary Health. This virtual issue will present a set of recently published papers from the Australian Journal of Primary Health that have been selected for their relevance to issues faced in China. It will be launched on 11 September 2017. We have written this Editorial to set the scene for the special issue, and as a general overview of issues affecting one of our most influential neighbours in the region.

Influential neighbours? The role of semantic neighbours in word production

Influential neighbours? The role of semantic neighbours in word production

Influence maximization through adaptive seeding

In this monograph we survey the adaptive seeding methodology for influence maximization. Influence maximization is the challenge of spreading information effectively through influential users in a social network. In many applications, one is restricted to select influencers from a set of users who engaged with the topic being promoted, and due to the structure of social networks, these users often rank low in terms of their influence potential. An alternative approach one can consider is an adaptive method which selects users in a manner which targets their influential neighbors. The advantage of such an approach is that it leverages the friendship paradox in social networks: while users are often not influential themselves, they know someone who is. Adaptive seeding is a two-stage stochastic optimization framework, designed to leverage the friendship paradox to seed high influential nodes. This framework encompasses a rich set of algorithmic challenges at the intersection of stochastic optimization and submodular optimization. We discuss this method here, along with algorithmic approaches, the friendship paradox in random graph models, and experiments on adaptive seeding.

Influential Neighbor Selection in Collaborative Filtering

Influential Neighbor Selection in Collaborative Filtering

A study on the influential neighbors to maximize information diffusion in online social networks

The problem of spreading information is a topic of considerable recent interest, but the traditional influence maximization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbitrary k nodes in a network, we have developed a decentralized version of the influential maximization problem by influencing k neighbors rather than arbitrary users in the entire network. We present several practical strategies and evaluate their performance with a real dataset collected from Twitter during the 2010 UK election campaign. Our experimental results show that information can be efficiently propagated in online social networks using neighbors with a high propagation rate rather than those with a high number of neighbors. To examine the importance of using real propagation rates, we additionally performed an experiment under the same conditions except the use of synthetic propagation rates, which is widely used in studying the influence maximization problem and found that their results were significantly different from real-world experiences.