Influence Spread Research Articles

Cost effective approach to identify multiple influential spreaders based on the cycle structure in networks

Cost effective approach to identify multiple influential spreaders based on the cycle structure in networks

Stemming competitive influence spread in social networks through binary ions motion optimization

Stemming competitive influence spread in social networks through binary ions motion optimization

Steering the spread of influence adaptively in social networks via a discrete scheduled particle swarm optimization

Steering the spread of influence adaptively in social networks via a discrete scheduled particle swarm optimization

Boris Johnson and the “Populist Hypothesis” in the Conservative Party (UK)

One of the key features of the phenomenon of populism is the high level of personalization of political activity, the close connection between the spread and level of influence of a populist movement and the personality of its leader. This feature is not, of course, unique to populism, as charismatic leaders can rise to prominence by advocating value systems that are far from populism of any kind - conservative, for example, or by making a career within influential organizational structures with a strong tradition, such as the church. However, for a populist movement, charismatic leadership is almost always a necessary element. On the other hand, in the context of open, public political struggle, populist techniques and rhetoric become a powerful and sometimes necessary tool.

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and #P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, technologies based on Machine Learning (ML) have achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.

Influential risk spreaders and their contribution to the systemic risk in the cryptocurrency network

This paper studies the risk spreading ability of cryptocurrencies and their contribution to the systemic risk during the period from 2018 to 2022. Three different network centrality approaches are employed to measure the risk spreading ability of cryptocurrencies both in breadth and depth, and from perspectives of local and global information. We also measure the contribution of cryptocurrencies to the systemic risk via ΔCoV aR, and detect what contributes to the systemic risk. Our findings show that cryptocurrencies with larger risk spreading ability, as well as others that experience decline in prices or high turnover, contribute more to the systemic risk.

A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks.

Network epidemiology plays a fundamental role in understanding the relationship between network structure and epidemic dynamics, among which identifying influential spreaders is especially important. Most previous studies aim to propose a centrality measure based on network topology to reflect the influence of spreaders, which manifest limited universality. Machine learning enhances the identification of influential spreaders by combining multiple centralities. However, several centrality measures utilized in machine learning methods, such as closeness centrality, exhibit high computational complexity when confronted with large network sizes. Here, we propose a two-phase feature selection method for identifying influential spreaders with a reduced feature dimension. Depending on the definition of influential spreaders, we obtain the optimal feature combination for different synthetic networks. Our results demonstrate that when the datasets are mildly or moderately imbalanced, for Barabasi-Albert (BA) scale-free networks, the centralities' combination with the two-hop neighborhood is fundamental, and for Erdős-Rényi (ER) random graphs, the centralities' combination with the degree centrality is essential. Meanwhile, for Watts-Strogatz (WS) small world networks, feature selection is unnecessary. We also conduct experiments on real-world networks, and the features selected display a high similarity with synthetic networks. Our method provides a new path for identifying superspreaders for the control of epidemics.

K＋＋ Shell: Influence maximization in multilayer networks using community detection

Selecting influential users in a network is essential to spread information quickly. Identifying influential users is very useful for viral marketing and brand communication. Influence maximization (IM) is selecting a few influential users in the network who can maximize the influence spread. Many existing algorithms address IM in single-layer networks. However, the study of IM in multi-layer networks is gaining importance after the advancement and rapid growth in the usage of online social networks. Studying IM in multi-layer networks in the context of viral marketing will be interesting. Motivated by this, this paper investigates the K＋＋ Shell decomposition algorithm to find the k set of influential nodes (seed nodes) in a multi-layer network. The proposed model prunes the nodes based on degree and assign reward points to their neighbors. We conducted a comparative study of various IM algorithms and reported the results. We observed that the K＋＋ Shell decomposition algorithm outperforms other algorithms on various real-time datasets under various settings and environments.

Identifying top influential spreaders based on the influence weight of layers in multiplex networks

Detecting influential nodes in multiplex networks is a complex task due to the presence of multiple layers. In this study, we propose a method for identifying important layers with strong spreaders based on several key parameters. These include a layer's position within a well-connected neighborhood, the number of active edges and critical nodes, the ratio of active nodes to all possible connections, and the intersection of intra-layer communication compared to other layers. To accomplish this, we have formulated a layer weighting method which takes into account these parameters, and developed an algorithm for mapping and computing the rank of nodes based on their spreading capability within multiplex networks. The resulting layer weighting is then used to map and compress centrality vector values to a scalar value, allowing us to calculate node centrality in multiplex networks via a coupled set of equations. Moreover, our method combines the important layer parameters to compute the influence of nodes from different layers. Our experimental results, conducted on both synthetic and real-world networks, demonstrate that the proposed approach significantly outperforms existing methods in detecting high influential spreaders. These findings highlight the importance of using a suitable layer weighting measure for identifying potential spreaders in multiplex networks.

Identifying influential spreaders in complex networks based on limited spreading domain

In recent years, the problem of influential spreader identification in complex networks has attracted extensive attention as its fundamental role in social network analysis, rumor controlling, viral marketing and other related fields. Centrality measures that consider different scales of neighborhood are commonly utilized for ranking node influence. The 2-hop neighborhood of the target node is deemed a suitable evaluation metric. However, as the network scale expands, only considering 2-hop neighborhood is overly restrictive. Furthermore, the interconnections among nodes are often disregarded. In this article, a new method named Limited Spreading Domain (LSD) is proposed to identify influential spreaders. LSD defines the target node’s 2-hop neighborhood as basic domain and takes the neighbors who are 3–6 hops away from target node as extended domain. The influence of target node is modeled as diffusion along the paths with limited length in basic domain and extended domain. A series of experiments are conducted in eight real complex networks and results demonstrate that LSD outperforms common centralities in terms of accuracy, stability,distinguishability and scalability.

Identify Influential Spreaders in Asymmetrically Interacting Multiplex Networks

Identifying the most influential spreaders is important to understand and control the spreading process in a network. As many real-world complex systems can be modeled as multilayer networks, the question of identifying important nodes in multilayer network has attracted much attention. Existing studies focus on the multilayer network structure, while neglecting how the structural and dynamical coupling of multiple layers influence the dynamical importance of nodes in the network. Here we investigate on this question in an information-disease coupled spreading dynamics on multiplex networks. Firstly, we explicitly reveal that three interlayer coupling factors, which are the two-layer relative spreading speed, the interlayer coupling strength and the two-layer degree correlation, significantly impact the spreading influence of a node on the contact layer. The suppression effect from the information layer makes the structural centrality on the contact layer fail to predict the spreading influence of nodes in the multiplex network. Then by mapping the coevolving spreading dynamics into percolation process and using the message-passing approach, we propose a method to calculate the size of the disease outbreaks from a single seed node, which can be used to estimate the nodes' spreading influence in the coevolving dynamics. Our work provides insights on the importance of nodes in the multiplex network and gives a feasible framework to investigate influential spreaders in the asymmetrically coevolving dynamics.

The Nodes Influence Maximization in Open Source Software Community Based on Probability Propagation Model

With the continuous development of Internet technology, open source software is more and more popular. Users and their interactions in a specific network plat constitute an open source software community, which can be represented by a complex network. It is of great significance to effectively and quickly identify the most influential users in the community, such as better guiding the management and development of the community, accelerating the information dissemination and personalized recommendation. Traditional methods of measuring node influence only consider some topology characteristics of the network, so the results are one-sided. Because there are different interactive behaviors among users in the community, it is necessary to mine more interaction information, so as to give a more comprehensive measure of node influence. In view of this, this paper proposes a method to maximize the node influence of open source software community based on a probability propagation model. Firstly, according to the relationship of users and their interactive records on projects, this paper quantifies the feedback of users on the project from three aspects (i.e., approve, save and modify) and establishes a new probability propagation model between users. Secondly, this paper proposes an algorithm(SIUF) to evaluate users' influence in the open source software community based on the probability propagation model. The algorithm fully considers the interaction behavior of users in the community. In the first stage, the user's own activity is taken as the initial ranking. In the second stage, the influence of neighbor nodes is taken into account, and the SIUF value of nodes is accumulated while the rich club effect is weakened. Finally, the proposed theory and method are applied to GitHub, a typical open source software community, and the correctness and effectiveness of this method are verified from influence spread and speed.

Triangular Stability Maximization by Influence Spread over Social Networks

In many real-world applications such as social network analysis and online advertising/marketing, one of the most important and popular problems is called influence maximization (IM), which finds a set of k seed users that maximize the expected number of influenced user nodes. In practice, however, maximizing the number of influenced nodes may be far from satisfactory for real applications such as opinion promotion and collective buying. In this paper, we explore the importance of stability and triangles in social networks, and formulate a novel problem in the influence spread scenario, named triangular stability maximization , over social networks, and generalize it to a general triangle influence maximization problem, which is proved to be NP-hard. We develop an efficient reverse influence sampling (RIS) based framework for the triangle IM with theoretical guarantees. To enable unbiased estimators, it demands probabilistic sampling of triangles, that is, sampling triangles according to their probabilities. We propose an edge-based triple sampling approach, which is exactly equivalent to probabilistic sampling and avoids costly triangle enumeration and materialization. We also design several pruning and reduction techniques, as well as a cost-model-guided heuristic algorithm. Extensive experiments and a case study over real-world graphs confirm the effectiveness of our proposed algorithms and the superiority of triangular stability maximization and triangle influence maximization.

Maximizing Influence Spread through a Dynamic Social Network (Student Abstract)

Modern social networks are dynamic in their nature; a new connections are appearing and old connections are disappearing all the time. However, in our algorithmic and complexity studies, we usually model social networks as static graphs. In this paper, we propose a new paradigm for the study of the well-known Target Set Selection problem, which is a fundamental problem in viral marketing and the spread of opinion through social networks. In particular, we use temporal graphs to capture the dynamic nature of social networks. We show that the temporal interpretation is, unsurprisingly, NP-complete in general. Then, we study computational complexity of this problem for multiple restrictions of both the threshold function and the underlying graph structure and provide multiple hardness lower-bounds.

Edge-weights-based method to identify influential spreaders in complex networks

Identifying influential nodes has drawn great attention in recent years. In this paper, a novel method for identifying influential spreaders based on potential edge weights (WDK for simplicity) for both undirected and unweighted networks is proposed. Degree and k-shell of a node and its neighbors are considered simultaneously, which are regarded as the weight of the edge directly connected to the node. The algorithm considers not only the local information of nodes but also their location information. The proposed method not only improves the accuracy of node mining but also has approximately linear time complexity, which indicates that the proposed method is suitable for large-scale networks. In order to validate the effectiveness of the proposed method, different evaluation indexes are introduced in nine real networks. Compared with five classical key nodes identification methods, the experimental results show that the proposed method performs optimally in all networks.

Does emergency policy mend the fold after the sheep have been stolen?- an empirical study on the Baiyin Cross-country race and its subsequent circuit breaker mechanism

This study examines the biggest disaster in the history of international trail running—the Huangshi Cross-Country Race in Baiyin city, China—as an example to quantitatively evaluate this large-scale sporting event's impact on the local economy. After calculating the city stock market returns based on the improved worst-case Value-at-Risk (WCVaR) designed for this study, this study proposes a hybrid forecasting method combining time series modelling and neural network autoregression (NNAR) models to quantify the net effect of the race disaster, through counterfactual information. The study discoveres that although the event's pre-publicity period positively stimulated Baiyin city's economy, it still could not offset the negative impact of unexpected events during the race. Then, the study further explores the net economic effect of the circuit breaker mechanism as a strategic tool in an attempt to quell the above-mentioned negative effects after the cross-country race, through applying a difference-in-differences method to regress and analyse the stock market yield panel data of Baiyin and its control cities before and after the policy's implementation. Subsequently, it was revealed that the circuit breaker mechanism could not effectively curb the spread of the disaster's negative influence, in other word, the circuit breaker exists only to ‘mend the fold’ after the ‘sheep’ are endangered and it is already too late.

A Hurst‐based diffusion model using time series characteristics for influence maximization in social networks

AbstractOnline social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, which pertains to finding methods for maximizing the spread of information (influence) across an OSN. Existing works in IM widely use a pre‐defined edge propagation probability for node activation. Hurst exponent (H), which depicts the self‐similarity in the time series depicting a user's past interaction behaviour, has also been used as activation criteria. In this work, we propose a Time Series Characteristic based Hurst‐based Diffusion Model (TSC‐HDM), which calculates H based on the stationary or non‐stationary characteristic of the time series. TSC‐HDM selects a handful of seed nodes and activates a seed node's inactive successor only if H > 0.5. The proposed model has been tested on four real‐world OSN datasets. The results have been compared against four other IM models – Independent Cascade, Weighted Cascade, Trivalency, and Hurst‐based Influence Maximization. TSC‐HDM is found to have achieved as much as 590% higher expected influence spread as compared to the other models. Moreover, TSC‐HDM has attained 344% better average influence spread than other state‐of‐the‐art models namely LIR, A‐Greedy, LPIMA, Genetic Algorithm with Dynamic Probabilities, NeighborsRemove, DegreeDecrease, IGIM, IRR, and PHG.

A depth-based heuristic to solve the multi-objective influence spread problem using particle swarm optimization

A depth-based heuristic to solve the multi-objective influence spread problem using particle swarm optimization

Identifying Influential Brokers on Social Media from Social Network Structure

Identifying influencers in a given social network has become an important research problem for various applications, including accelerating the spread of information in viral marketing and preventing the spread of fake news and rumors. The literature contains a rich body of studies on identifying influential source spreaders who can spread their own messages to many other nodes. In contrast, the identification of influential brokers who can spread other nodes' messages to many nodes has not been fully explored. Theoretical and empirical studies suggest that involvement of both influential source spreaders and brokers is a key to facilitating large-scale information diffusion cascades. Therefore, this paper explores ways to identify influential brokers from a given social network. By using three social media datasets, we investigate the characteristics of influential brokers by comparing them with influential source spreaders and central nodes obtained from centrality measures. Our results show that (i) most of the influential source spreaders are not influential brokers (and vice versa) and (ii) the overlap between central nodes and influential brokers is small (less than 15%) in Twitter datasets. We also tackle the problem of identifying influential brokers from centrality measures and node embeddings, and we examine the effectiveness of social network features in the broker identification task. Our results show that (iii) although a single centrality measure cannot characterize influential brokers well, prediction models using node embedding features achieve F1 scores of 0.35--0.68, suggesting the effectiveness of social network features for identifying influential brokers.

Crisis Assessment Oriented Influence Maximization in Social Networks

Influence maximization (IM) aims to find a subset of k nodes that can maximize the final active node set under an information diffusion model. With the development and popularity of social networks, the IM problem plays an essential role in various applications, such as public opinion analysis, viral marketing, and rumor early warning. However, most of the existing IM solutions have not accessed the risk of negative information from nodes in the future. In reality, a company may face economic loss when negative information breaks out of its spokesman. Therefore, a crisis assessment oriented and topic-based IM problem (TIM-CA) is proposed, which is utilized to model the IM problem by considering the crisis assessment (CA) and topics of users. To solve this problem, we propose a maximum influence arborescence model-based algorithm for TIM-CA, namely, MIA-TIM-CA. The proposed algorithm consists of crisis degree calculation, topic relevance calculation, and influence spread evaluation. More importantly, as for crisis degree calculation, it considers self-, topology-, and topic-based crisis degrees for each node. At the influence spread evaluation stage, MIA-TIM-CA proposes two functions to evaluate the node's importance and node influence spread. Extensive experiments on two real-world social networks demonstrate that our MIA-TIM-CA outperforms all comparison algorithms on influence spread, crisis score, and running time.