Influence Spread Research Articles

Characterization of neuromuscular transmission and projections of muscle motor neurons in the rat stomach.

The stomach is the primary reservoir of the gastrointestinal tract, where ingested content is broken down into small particles. Coordinated relaxation and contraction is essential for rhythmic motility and digestion, but how the muscle motor innervation is organized to provide appropriate graded regional control is not established. In this study, we recorded neuromuscular transmission to the circular muscle using intracellular microelectrodes to investigate the spread of the influence of intrinsic motor neurons. In addition, microanatomical investigations of neuronal projections and pharmacological analysis were conducted to investigate neuromuscular relationships. We found that inhibitory neurotransmission to the circular muscle is graded with stimulus strength and circumferential distance from the stimulation site. The influence of inhibitory neurons declined between 1 and 11 mm from the stimulation site. In the antrum, corpus, and fundus, the declines at 11 mm were about 20%, 30%, and 50%, respectively. Stimulation of inhibitory neurons elicited biphasic hyperpolarizing potentials often followed by prolonged depolarizing events in the distal stomach, but only hyperpolarizing events in the proximal stomach. Excitatory neurotransmission influence varied greatly between proximal stomach, where depolarizing events occurred, and distal stomach, where no direct electrical effects in the muscle were observed. Structural studies using microlesion surgeries confirmed a dominant circumferential projection. We conclude that motor neuron influences extend around the gastric circumference, that the effectiveness can be graded by the recruitment of different numbers of motor neuron nerve terminals to finely control gastric motility, and that the ways in which the neurons influence the muscle differ between anatomical regions.NEW & NOTEWORTHY This study provides a detailed mapping of nerve transmission to the circular muscle of the different anatomical regions of rat stomach. It shows that excitatory and inhibitory influences extend around the gastric circumference and that there is a summation of neural influence that allows for finely graded control of muscle tension and length. Nerve-mediated electrical events are qualitatively and quantitatively different between regions, for example, excitatory neurons have direct effects on fundus but not antral muscle.

A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective

A study of the effect of influential spreaders on the different sectors of Indian market and a few foreign markets: a complex networks perspective

Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach

Given a graph G, a cost associated with each node, and a budget B, the budgeted influence maximization (BIM) aims to find the optimal set S of seed nodes that maximizes the influence among all possible sets such that the total cost of nodes in S is no larger than B. Existing solutions mainly follow the non-adaptive idea, i.e., determining all the seeds before observing any actual diffusion. Due to the absence of actual diffusion information, they may result in unsatisfactory influence spread. Motivated by the limitation of existing solutions, in this paper, we make the first attempt to solve the BIM problem under the adaptive setting, where seed nodes are iteratively selected after observing the diffusion result of the previous seeds. We design the first practical algorithm which achieves an expected approximation guarantee by probabilistically adopting a cost-aware greedy idea or a single influential node. Further, we develop an optimized version to improve its practical performance in terms of influence spread. Besides, the scalability issues of the adaptive IM-related problems still remain open. It is because they usually involve multiple rounds (e.g., equal to the number of seeds) and in each round, they have to construct sufficient new reverse-reachable set (RR-set) samples such that the claimed approximation guarantee can actually hold. However, this incurs prohibitive computation, imposing limitations on real applications. To solve this dilemma, we propose an incremental update approach. Specifically, it maintains extra construction information when building RR-sets, and then it can quickly correct a problematic RR-set from the very step where it is first affected. As a result, we recycle the RR-sets at a small computational cost, while still providing correctness guarantee. Finally, extensive experiments on large-scale real graphs demonstrate the superiority of our algorithms over baselines in terms of both influence spread and running time.

Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm

Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing and helps significantly in social media advertising. Previous Greedy and Reverse Influence Sampling-based IM approaches are ineffective in real-world social networks due to their significant computational cost and execution time. Further, even heuristic approaches applied to IM generally yield minimal performance gain relative to the decreased time complexity. This presents a challenge in developing cost-effective algorithms with low execution time that can handle diverse social networks. In this paper, we propose the discretization of the nature-inspired Harris’ Hawks Optimization meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris’ Hawks’ intelligence, we employ a neighbor scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace, reducing the total computational cost. We evaluate the efficacy of our proposed DHHO approach on eight social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches.

Evaluating Roman influence in southeast Europe by zooarchaeological meta-analysis

This study uses increased specialisation in husbandry practice and the development of larger livestock as a proxy for spatial and temporal variation in Roman influence in southeast Europe. Data are presented from three regions subject to varying levels of Roman control: (a) the Balkan provinces, under long-term occupation; (b) Dacia, a province from 106 to 271/275 CE; and (c) regions beyond the Empire to the northwest and northeast. While little change in husbandry practice occurs during the early Roman occupation in the first centuries BCE and CE, clear changes are evident across occupied regions in the second and third centuries CE. A cattle-focused economic system develops, likely influenced by an increased focus on arable production, and a significant increase in cattle and sheep/goat size is observed. In the Balkan provinces, while larger livestock persist into the early Byzantine period, there is a decline in specialised cattle exploitation at rural sites in the late Roman period, and at urban sites in the early Byzantine period, concurrent with a decrease in Roman political control. In Dacia, in contrast, average livestock size decreases after the Roman withdrawal, while specialised cattle husbandry persists for several centuries. Beyond the Empire, specialised cattle husbandry is evident from the late Iron Age, but increases in the second and third centuries, perhaps indicating the spread of Roman influence beyond occupied regions.

Leveraging neighborhood and path information for influential spreaders recognition in complex networks

Leveraging neighborhood and path information for influential spreaders recognition in complex networks

Fast and Space-Efficient Parallel Algorithms for Influence Maximization

Influence Maximization (IM) is a crucial problem in data science. The goal is to find a fixed-size set of highly influential seed vertices on a network to maximize the influence spread along the edges. While IM is NP-hard on commonly used diffusion models, a greedy algorithm can achieve (1 - 1/ e )-approximation by repeatedly selecting the vertex with the highest marginal gain in influence as the seed. However, we observe two performance issues in the existing work that prevent them from scaling to today's large-scale graphs: space-inefficient memorization to estimate marginal gain, and time-inefficient seed selection process due to a lack of parallelism. This paper significantly improves the scalability of IM using two key techniques. The first is a sketch-compression technique for the independent cascading model on undirected graphs. It allows combining the simulation and sketching approaches to achieve a time-space tradeoff. The second technique includes new data structures for parallel seed selection. Using our new approaches, we implemented PaC-IM : Parallel and Compressed IM. We compare PaC-IM with state-of-the-art parallel IM systems on a 96-core machine with 1.5TB memory. PaC-IM can process the ClueWeb graph with 978M vertices and 75B edges in about 2 hours. On average, across all tested graphs, our uncompressed version is 5--18x faster and about 1.4x more space-efficient than existing parallel IM systems. Using compression further saves 3.8x space with only 70% overhead in time on average.

Alternate Solutions for Influence Maximization: Beyond theoretical approximation by the Genetic Algorithm Framework

As a special case of Social Influence Maximization (SIM) problems, the top k-influencers problem aims to identify the top k actors, called members of the seed set, in a network whose endorsement of a given action causes it to be chosen by the largest possible set of the network’s members, whose cardinality is called the influence spread (IS). Greedy framework algorithms like StaticGreedyCELF deliver a ground-truth solution with a proven approximation. This manuscript aims to investigate solutions for SIM with advantages comparable to or superior to those of CELF’s solutions, such as the lower propagation time and variance of the IS. Novel models to discover alternatives to the CELF’s solution in the forms of the Risk-Averse (RAIM), Risk-Least (RLIM), Time-Least (TLIM), and Risk-Blind Influence Maximization (RBIM) models among solutions non-dominated by the CELF’s solution are proposed. A Genetic Algorithm Framework for Influence Maximization (GAFIM) is outlined, the only framework in the literature capable of achieving near-optimal solutions for the RAIM, RLIM, TLIM, and RBIM models under the famous diffusion models. A novel parameter tuning strategy offers GAFIM a class of optimal states rather than a single ideal state, allowing better exploration in each proposed model under each diffusion model. Notable findings from the research summary include (a) uncertainty in discovering an alternative under all conditions; (b) the favorable effect of expanding the seed set; (c) challenges in dominating CELF with RBIM; (d) a homogeneous variance distribution with no greater influence depth among alternatives; and (e) the positive side effects of models.

Balanced influence maximization in social networks based on deep reinforcement learning

Balanced influence maximization aims to balance the influence maximization of multiple different entities in social networks and avoid the emergence of filter bubbles and echo chambers. Recently, an increasing number of studies have drawn attention to the study of balanced influence maximization in social networks and achieves success to some extent. However, most of them still have two major shortcomings. First, the previous works mainly focus on spreading the influence of multiple target entities to more users, ignoring the potential influence of the correlation between the target entities and other entities on information propagation in real social networks. Second, the existing methods require a large amount of diffusion sampling for influence estimation, making it difficult to apply to large social networks. To this end, we propose a Balanced Influence Maximization framework based on Deep Reinforcement Learning named BIM-DRL, which consists of two core components: an entity correlation evaluation module and a balanced seed node selection module. Specifically, in the entity correlation evaluation module, an entity correlation evaluation model based on the users’ historical behavior sequences is proposed, which can accurately evaluate the impact of entity correlation on information propagation. In the balanced seed node selection module, a balanced influence maximization model based on deep reinforcement learning is designed to train the parameters in the objective function, and then a set of seed nodes that maximize the balanced influence is found. Extensive experiments on six real-life network datasets demonstrate the superiority of the BIM-DRL over state-of-the-art methods on the metrics of balanced influence spread and balanced propagation accuracy.

The spatiotemporal analysis of the population migration network in China, 2021

Population migration is a critical component of large-scale spatiotemporal models of infectious disease transmission. Identifying the most influential spreaders in networks is vital to controlling and understanding the spreading process of infectious diseases. We used Baidu Migration data for the whole year of 2021 to build mobility networks. The nodes of the network represent cities, and the edges represent the population flow between cities. By applying the k-shell decomposition and the Louvain algorithm, we could get the k-shell values for each city and community partition. Then, we identified the most efficient nodes or pathways in a complex network by generating random networks. Furthermore, we analyzed the eigenvalue of the migration matrix to find the nodes that have the most impact on the network. We also found the consistency between k-shell value and eigenvalue through Kendall's τ test. The main result is that in Spring Festival and National Day, the network is at higher risk of an infectious disease outbreak and the Yangtze River Delta is at the highest risk of an epidemic all year around. Shanghai is the most significant node in both k-shell value and eigenvalue analysis. The spatiotemporal property of the network should be taken into account to model the transmission of infectious diseases more accurately.

Identifying critical nodes via link equations and deep reinforcement learning

Identifying an optimal set of nodes that can maximize the spread of influence in a network is a crucial challenge in network science. It has numerous applications such as epidemic control and rumor containment. However, most existing techniques are limited by their high computational costs, making them impractical for graphs with millions of nodes. Moreover, the previous approaches have primarily focused on the structural characteristics of the network while the characteristics of information diffusion are ignored. This paper proposes a deep reinforcement learning framework, DeepELE, to bridge these gaps. DeepELE incorporates a graph embedding technique to represent the graph states and applies a deep reinforcement learning method to learn the policy automatically. Note that we assess the contribution of links to spreading processes and further account for the diffusion-related contribution along with the graph structure information into convolutional neural and the Q network. Extensive experiments on both synthetic and real-world networks validate the efficiency and efficacy of DeepELE. The results demonstrate that DeepELE significantly outperforms the state-of-the-art methods, especially for large-scale networks with millions of nodes.

Identifying influential nodes through an improved k-shell iteration factor model

Measuring the importance of nodes is a rapidly developing field in network science, with centrality measures playing a critical role in various applications, including epidemic control, influence maximization, and network integration. The commonly used coreness calculated by the k-core decomposition process for identifying influential spreaders has limitations, which fail to consider the topological position of nodes and lead to low node discrimination. To address these issues, we propose a novel approach called the structural iteration factor ranking method to identify the influential nodes in complex networks, which considers the remaining structural properties when implementing the k-core decomposition. Our method considers both local and global hierarchy information and selects the neighbor information attribute to avoid problems with overlapping neighbor information propagation caused by the significant clustering coefficient of networks. We consider ground-truth influence ranking based on the Susceptible–Infected–Recovered model, on which the performance of the proposed ranking method is verified. Our proposed method outperforms previous methods in calculating monotonicity, similarity, and accuracy. It provides a more comprehensive evaluation of nodes and enables more in-depth identification of critical nodes. These findings imply that considering the remaining structural properties when implementing the k-core decomposition dominates the spreading strength of nodes and catches more details for ranking the node influence in complex networks.

Identifying and Ranking Influential Spreaders in Complex Networks by Localized Decreasing Gravity Model

Abstract Identifying crucial nodes in complex networks is paid more attention in recent years. Some classical methods, such as degree centrality, betweenness centrality and closeness centrality, have their advantages and disadvantages. Recently, the gravity model is applied to describe the relationship of nodes in a complex network. However, the interaction force in gravity model follows the square law of distance, which is inconsistent with the actual situation. Most people are generally affected by those who are surrounding them, which means that local influence should be emphasized. To address this issue, we propose an indexing method called localized decreasing gravity centrality by maximizing the local influence of a node. In the proposed measure, the mass and radius of gravity model are redefined, which can represent the spreading ability of the node. In addition, a decreasing weight is added to strengthen the local influence of a node. To evaluate the performance of the proposed method, we utilize four different types of networks, including interaction networks, economic networks, collaboration networks and animal social networks. Also, two different infectious disease models, susceptible-infectious-recovered (SIR) and susceptible-exposed-low risk-high risk-recovered (SELHR), are utilized to examine the spreading ability of influential nodes.

A Search Space Reduction-Based Progressive Evolutionary Algorithm for Influence Maximization in Social Networks

Influence maximization (IM) problem, which selects a subset of nodes from a social network to maximize the influence spread, appeals to numerous scholars. Since the IM problem is NP-hard, it is still an arduous task to achieve good results in terms of influence spread and running time at the same time. This article proposes a novel search space reduction strategy-based progressive evolutionary algorithm (SSR-PEA) for solving IM problems effectively and efficiently. In SSR-PEA, a novel search space reduction strategy (SSR) in the light of the power-law distribution of the social network is designed to reduce the computational overheads, which eliminates a great deal of less influential nodes in a sensible way. After that, we propose a progressive evolutionary framework based on SSR, where the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-element individual is optimized on the basis of the (<inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-1)-element individual to speed up the optimal solution search process. Experimental results on ten real-world networks demonstrate that the proposed algorithm SSR-PEA can achieve 98<inline-formula> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the influence spread achieved by cost-effective lazy forward (CELF) on average, and its running time is two or even three orders of magnitude shorter. Thus, SSR-PEA strikes a better balance between effectiveness and efficiency.

GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case

The growth of COVID-19, caused by the SARS-CoV-2 virus, has turned into an unprecedented pandemic in the last century. It is crucial to identify superspreading nodes to prevent the pandemic&#x2019;s progress. Most available superspreader identification techniques consider only a single or few network metrics related to the complex network&#x2019;s topological structure. Furthermore, it is more challenging to determine influential spreaders from heterogeneous structures of networks. In a disease transmission network, the degree of heterogeneity is essential to locate the path of the infection spread. Therefore, it is required to have an extended degree of centrality to collect information from various neighborhood levels. This article presents an approach, namely, global structure influence (<inline-formula> <tex-math notation="LaTeX">${\rm GSI}$</tex-math> </inline-formula>), which considers network nodes&#x2019; local and global influence. This method can gather information from multiple levels of the neighborhood. Evaluation of our proposed method is done by considering different types of networks, i.e., social networks, highly heterogeneous human contact networks, and epidemiological networks, and also by using the benchmark susceptible-infected-recovered (SIR) epidemic model. The <inline-formula> <tex-math notation="LaTeX">${\rm GSI}$</tex-math> </inline-formula> technique provides real-spreading dynamics across various network structures and has outperformed the baseline techniques with an average Kendall&#x2019;s <inline-formula> <tex-math notation="LaTeX">$\tau$</tex-math> </inline-formula> improvement range from 0.017 to 0.278. This study will help to identify the superspeaders in real applications, where pathogens spread quickly because of close contact, such as the recently witnessed COVID-19 pandemic.

Deadline-aware misinformation prevention in social networks with time-decaying influence

A misinformation prevention problem is essential in social networks since misinformation could greatly mislead people and interfere societal, economical, or even political circumstances. Traditional diffusion models for describing information spread assume that active individuals have only one trial to activate their friends. This may oversimplify the mechanism of the influence between two individuals in reality, i.e., an individual can have more than one chance to influence its neighbors. In addition, the influence between nodes is considered as a constant, although it is inherently dynamic in nature. This paper addresses the deadline-aware misinformation prevention problem considering a more realistic diffusion model, i.e., competitive dynamic independent cascade model (CDIC), which allows multiple activation trials with time-decaying influence power. We further show that the CDIC preserves the monotone and submodular properties. The deadline-aware misinformation prevention problem aims to find some influential truth spreaders such that the expected number of nodes to be saved or protected from misinformation is maximized before a given deadline. An adaptive sampling approach (ASA) is proposed under the CDIC model for solving the problem, which outperforms the greedy approach with lazy evaluations by several orders of magnitude in terms of the computational efficiency through a series of simulation on real datasets.

Identifying influential nodes in complex networks using a gravity model based on the H-index method

Identifying influential spreaders in complex networks is a widely discussed topic in the field of network science. Numerous methods have been proposed to rank key nodes in the network, and while gravity-based models often perform well, most existing gravity-based methods either rely on node degree, k-shell values, or a combination of both to differentiate node importance without considering the overall impact of neighboring nodes. Relying solely on a node's individual characteristics to identify influential spreaders has proven to be insufficient. To address this issue, we propose a new gravity centrality method called HVGC, based on the H-index. Our approach considers the impact of neighboring nodes, path information between nodes, and the positional information of nodes within the network. Additionally, it is better able to identify nodes with smaller k-shell values that act as bridges between different parts of the network, making it a more reasonable measure compared to previous gravity centrality methods. We conducted several experiments on 10 real networks and observed that our method outperformed previously proposed methods in evaluating the importance of nodes in complex networks.

Influence spreading model for partial breakthrough effects on complex networks

Breakthrough effects are a crucial part of many kinds of influence spreading, such as social or infectious contagion. We introduce a novel model that can accurately simulate influence spreading on complex networks with partial breakthrough happening at a given probability. The novel model unifies our earlier analytical and simulation versions of the model that are only applicable to a fixed-breakthrough scenario. A wide range of applications in, for example, social influence and epidemic spreading analysis are enabled by the ability to consider partial breakthrough effects. The breakthrough effects of the new model are controlled by an arbitrary breakthrough probability that determines how likely it is for a node to get reinfluenced. We demonstrate our model on real-world social network structures and provide an example application in the study of epidemic spreading.

Link prediction for ex ante influence maximization on temporal networks

Influence maximization (IM) is the task of finding the most important nodes in order to maximize the spread of influence or information on a network. This task is typically studied on static or temporal networks where the complete topology of the graph is known. In practice, however, the seed nodes must be selected before observing the future evolution of the network. In this work, we consider this realistic ex ante setting where p time steps of the network have been observed before selecting the seed nodes. Then the influence is calculated after the network continues to evolve for a total of T>p time steps. We address this problem by using statistical, non-negative matrix factorization and graph neural networks link prediction algorithms to predict the future evolution of the network, and then apply existing influence maximization algorithms on the predicted networks. Additionally, the output of the link prediction methods can be used to construct novel IM algorithms. We apply the proposed methods to eight real-world and synthetic networks to compare their performance using the susceptible-infected (SI) diffusion model. We demonstrate that it is possible to construct quality seed sets in the ex ante setting as we achieve influence spread within 87% of the optimal spread on seven of eight network. In many settings, choosing seed nodes based only historical edges provides results comparable to the results treating the future graph snapshots as known. The proposed heuristics based on the link prediction model are also some of the best-performing methods. These findings indicate that, for these eight networks under the SI model, the latent process which determines the most influential nodes may not have large temporal variation. Thus, knowing the future status of the network is not necessary to obtain good results for ex ante IM.

Exploiting community and structural hole spanner for influence maximization in social networks

AbstractViral marketing is a frequently used social marketing strategy which aims to promote products on various social media. In order to devise an efficient viral marketing strategy, influence maximization problem is widely studied in social networks. Influence maximization problem tries to find a set of influential users who could influence social users maximally. However, existed influence maximization algorithms could not satisfy the needs of social marketers well. In social networks, community structure is an important feature where social users are closely connected together in groups, and structural hole spanners are those users who bridge different communities. In order to balance the effectiveness and efficiency of viral marketing in social networks, in this paper, we present to exploit the community structure and structural hole spanners in social networks for solving the influence maximization problem. Different to traditional algorithms, we devise a strategy to approximate social influences of social users to find seeds efficiently. In particular, for an internal user inside a community in social networks, we utilize his social influence to members inside this community to approximate his social influence in the whole social network. For a structural hole spanner who bridges multiple communities, we utilize his social influence to members in these communities to approximate his social influence in the whole network. Based on the approximate social influence, information diffusion model and greedy framework, we propose a Community and Structural Hole Spanner based Greedy (CSHS‐G) algorithm which devises several influence lists to store social influences of users and fast computation scheme to find seeds. Besides, to further improve the efficiency, we propose a Community and Structural Hole Spanner based Heuristic (CSHS‐H) algorithm which approximates social influences of structural hole spanners by utilizing their 2‐hops influences. We conduct a comprehensive performance evaluation on the crawled real‐world data set. Experimental results show that, compared to all of the baseline algorithms, our proposed algorithms not only have high efficiency, but also could guarantee larger influence spread.