Identification Of Influential Spreaders Research Articles

New metrics for influential spreaders identification in complex networks based on D-spectra of nodes

Identifying important nodes is of key significance in network sciences as it is closely related to information and disease spreading, structural robustness, etc. Till today, no theoretically proved optimal metric for measuring the importance of nodes is known, while a lot of metrics, mostly heuristic, have been studied. In this paper, we propose several heuristic metrics that are constructed from D-spectra of vertices for discussion. The latter is a graph invariant which is induced by D-chain decompositions of graphs, a recently introduced framework for studying network structures by the first author, Bura and Reidys (SIAM J. Appl. Dyn. Syst. 18, 2019, pp. 2181–2201). Statistical analyses based on numerous data from running the SIR model on real-world and random networks show that some of our proposed metrics outperform a number of well-known metrics such as the H-index, the core values, the betweenness centrality and the closeness centrality.

Identifying influential spreaders in complex networks based on density entropy and community structure

In recent years, exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers, given its fundamental theoretical significance and practical implications. To address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities, this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure (DECS). The proposed method initially integrates a community detection algorithm to obtain the community partition results of the networks. It then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its influence. Experimental validation is conducted on eight networks of varying sizes through susceptible–infected–recovered (SIR) propagation experiments and network static attack experiments. The experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions, particularly in terms of information spreading capability, thereby enhancing the accurate identification of critical nodes in networks.

Identifying influential nodes in spreading process in higher-order networks

Identifying influential nodes in spreading process in the network is an important step to control the speed and range of spreading, which can be used to accelerate the spread of beneficial information such as healthy behaviors, innovations and suppress the spread of epidemics, rumors and fake news. Existing researches on identification of influential spreaders are mostly based on low-order complex networks with pairwise interactions. However, interactions between individuals occur not only between pairwise nodes but also in groups of three or more nodes, which introduces complex mechanism of reinforcement and indirect influence. The higher-order networks such as simplicial complexes and hypergraphs, can describe features of interactions that go beyond the limitation of pairwise interactions. Currently, there are relatively few researches of identifying influential spreaders in higher-order networks. Some centralities of nodes such as higher-order degree centrality and eigenvector centrality are proposed, but they mostly consider only the network structure. As for identification of influential spreaders, the spreading influence of a node is closely related to the spreading process. In this paper, we work on identification of influential spreaders on simplicial complexes by taking both network structure and dynamical process into consideration. Firstly, we quantitatively describe the dynamics of disease spreading on simplicial complexes by using the Susceptible-Infected-Recovered microscopic Markov equations. Next, we use the microscopic Markov equations to calculate the probability that a node is infected in the spreading process, which is defined as the spreading centrality (SC) of nodes. This spreading centrality involves both the structure of simplicial complex and the dynamical process on it, and is then used to rank the spreading influence of nodes. Simulation results on two types of synthetic simplicial complexes and four real simplicial complexes show that compared with the existing centralities on higher-order networks and the optimal centralities of collective influence and nonbacktracking centrality in complex networks, the proposed spreading centrality can more accurately identify the most influential spreaders in simplicial complexes. In addition, we find that the probability of nodes infected is highly positively correlated with its influence, which is because disease preferentially reaches nodes with many contacts, who can in turn infect their many neighbors and become influential spreaders.

Influential Spreaders Identification by Fusing Network Topology

With the development of network science, complex network analysis has received extensive attention. In recent years, influential spreaders identification has become a hot topic in the research of complex networks. In general, influential spreader identification algorithms are mainly divided into centrality-based algorithms and topology-based algorithms. However, centrality-based algorithms have to face the information limitation problem that leads to low accuracy for identifying influential spreaders. Topology-based algorithms have both structural and positional limitation problems that lead to low accuracy for identifying peripheral influential spreaders. In this paper, we focus on improving the situation and propose two influential spreader identification algorithms, NSC (neighborhood structure centrality) and NPC (neighborhood position centrality) from both the perspective of the centrality and the network topology. NSC algorithm collects various types of network structure information through structure embedding and clustering, so as to solve missing network structure information problem. NPC algorithm calculates neighborhood location information by improving the k-shell algorithm to tackle location limitation problem. Experimental results with fourteen baseline algorithms show that our proposed algorithms NSC and NPC can achieve higher accuracy.

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.

Influential Spreaders Identification in Complex Networks With TOPSIS and K-Shell Decomposition

In view that the K-shell decomposition method can only effectively identify a single most influential node, but cannot accurately identify a group of most influential nodes, this article proposes a hybrid method based on K-shell decomposition to identify the most influential spreaders in complex networks. First, the K-shell decomposition method is used to decompose the network, and the network is regarded as a hierarchical structure from the inner core to the periphery core. Second, the existing centrality methods such as H-index are used as the secondary score of the proposed method to select nodes in each hierarchy of the network. In addition, for the sake of alleviating the overlapping problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced to calculate the comprehensive score of secondary score and overlapping range, and the node with the highest comprehensive score will be selected in each round. The proposed algorithm can be used as a general framework to improve the existing centrality method which can represent nodes with definite values of centrality. Experimental results show that in the susceptible–infected–recovered (SIR) model experiment, compared with the benchmark methods, the infection scale of the proposed K-TOPSIS method in nine real networks is improved by 1.15%, 2.23%, 1.95%, 3.12%, 6.29%, −0.37%, 4.01%, 0.48%, and 0.48%, respectively. The novel method is improved by 0.44, 1.18, 1.16, 11.30, 2.03, 2.53, 2.70, and 2.13 in average shortest path length experiment, respectively, except for Facebook network. It shows that the novel method is reasonable and effective.

Identifying multiple influential spreaders in complex networks based on spectral graph theory

One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network. The importance and dispersion of critical nodes among them are both vital factors that can influence maximization. We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory. This algorithm first quantifies the role played by the local structure of nodes in the propagation process, then classifies the nodes based on the eigenvectors of the Laplace matrix, and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other. Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.

Identifying influential spreaders by gravity model considering multi-characteristics of nodes

How to identify influential spreaders in complex networks is a topic of general interest in the field of network science. Therefore, it wins an increasing attention and many influential spreaders identification methods have been proposed so far. A significant number of experiments indicate that depending on a single characteristic of nodes to reliably identify influential spreaders is inadequate. As a result, a series of methods integrating multi-characteristics of nodes have been proposed. In this paper, we propose a gravity model that effectively integrates multi-characteristics of nodes. The number of neighbors, the influence of neighbors, the location of nodes, and the path information between nodes are all taken into consideration in our model. Compared with well-known state-of-the-art methods, empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on ten real networks suggest that our model generally performs best. Furthermore, the empirical results suggest that even if our model only considers the second-order neighborhood of nodes, it still performs very competitively.

Identifying influential spreaders in complex networks for disease spread and control

Identifying influential spreaders is an important task in controlling the spread of information and epidemic diseases in complex networks. Many recent studies have indicated that the identification of influential spreaders is dependent on the spreading dynamics. Finding a general optimal order of node importance ranking is difficult because of the complexity of network structures and the physical background of dynamics. In this paper, we use four metrics, namely, betweenness, degree, H-index, and coreness, to measure the central attributes of nodes for constructing the disease spreading models and target immunization strategies. Numerical simulations show that spreading processes based on betweenness centrality lead to the widest range of propagation and the smallest epidemic threshold for all six networks (including four real networks and two BA scale-free networks generated according to Barabasi–Albert algorithm). The target immunization strategy based on the betweenness centrality of nodes is the most effective for BA scale-free networks but displays poor immune effect for real networks in identifying the most important spreaders for disease control. The immunization strategy based on node degrees is the most effective for the four real networks. Findings show that the target immune strategy based on the betweenness centrality of nodes works best for standard scale-free networks, whereas that based on node degrees works best for other nonstandard scale-free networks. The results can provide insights into understanding the different metrics of measuring node importance in disease transmission and control.

Influential Spreaders Identification in Complex Networks with Improved Hybrid K-Shell Method

Influential Spreaders Identification in Complex Networks with Improved Hybrid K-Shell Method

Identifying influential spreaders in complex networks by an improved gravity model

Identification of influential spreaders is still a challenging issue in network science. Therefore, it attracts increasing attention from both computer science and physical societies, and many algorithms to identify influential spreaders have been proposed so far. Degree centrality, as the most widely used neighborhood-based centrality, was introduced into the network world to evaluate the spreading ability of nodes. However, degree centrality always assigns too many nodes with the same value, so it leads to the problem of resolution limitation in distinguishing the real influences of these nodes, which further affects the ranking efficiency of the algorithm. The k-shell decomposition method also faces the same problem. In order to solve the resolution limit problem, we propose a high-resolution index combining both degree centrality and the k-shell decomposition method. Furthermore, based on the proposed index and the well-known gravity law, we propose an improved gravity model to measure the importance of nodes in propagation dynamics. Experiments on ten real networks show that our model outperforms most of the state-of-the-art methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value.

A generalized gravity model for influential spreaders identification in complex networks

• A genrealized gravity model is proposed to identify influential speaders, which is the generalized form of the gravity model. • The parameter α can be modified to get better performance in real applications. • Encouraging results from experiments on four real-world networks demonstrate the effectiveness of the proposed method. How to identify influential spreaders in complex networks is still an open issue in network science. Many approaches from different perspectives have been proposed to identify vital nodes in complex networks. In these models, gravity model is an effective model to find vital nodes based on local information and path information. However, gravity model just uses degree of the node to judge local information, which is not precise. To address this issue, a generalized gravity model is proposed in this paper. Generalized gravity model measures local information from both local clustering coefficient and degree of each node, which is more comprehensive. Also, parameter α can be modified in different applications to get better performance. Generalized gravity model can degenerate into gravity model when α = 0 . Promising results from experiments on four real-world networks demonstrate the effectiveness of the proposed method.

Identification of Influential Spreaders in Social Networks using Improved Hybrid Rank Method

Abstract Online Social Networks provide us a gateway towards valuable information regarding personal preferences, interests, and connections. Ranking nodes by centrality measures help us to identify some of the most critical and influential nodes in a network who play a crucial role in the information spreading process and influence maximization. Traditional centrality measures have certain restraints and do not provide optimal results single-handedly. With the advances in research, leading to the formulation of the concept of hybrid centrality, the detection of the most influential spreaders in a network has shown a large scale of improvement. Some of the real-world phenomena such as viral marketing, rumor spreading, etc. can be addressed by leveraging the fact that only the dominant and authoritative nodes play a crucial role in propagating information about a new product or idea and those nodes can also help in blocking false information or rumors from reaching the other parts of the network. In this paper, we present the Improved Hybrid Rank algorithm, which combines two centralities, namely, the Extended Neighborhood Coreness centrality and the H-Index centrality. The results obtained by simulating our proposed method using the SIR (Susceptible-Infected-Recovered) model on different un-directed and directed real-world networks show that the ranking of nodes and the choice of influential spreaders, as proposed by our algorithm outperforms many other classical methods.

Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution.

The human interactome is instrumental in the systems-level study of the cell and the contextualization of disease-associated gene perturbations. However, reference organismal interactomes do not capture the cell-type-specific context in which proteins and modules preferentially act. Here, we introduce SCINET, a computational framework that reconstructs an ensemble of cell-type-specific interactomes by integrating a global, context-independent reference interactome with a single-cell gene-expression profile. SCINET addresses technical challenges of single-cell data by robustly imputing, transforming, and normalizing the initially noisy and sparse expression of data. Inferred cell-level gene interaction probabilities and group-level interaction strengths define cell-type-specific interactomes. We use SCINET to reconstruct and analyze interactomes of the major human brain and immune cell types, revealing specificity and modularity of perturbations associated with neurodegenerative, neuropsychiatric, and autoimmune disorders. We report cell-type interactomes for brain and immune cell types, together with the SCINET package.

Influential spreaders identification in complex networks with potential edge weight based k-shell degree neighborhood method

In social network analysis, one of the important aspects is to identify the nodes that are vital to the information diffusion process such as viral marketing, worm propagation in a computer network, disease spreading, etc. The degree centrality measures only the local influence of a node whereas, others like closeness and betweenness capture the global impact. K-shell centrality uses the position/location of a node in the network to estimate its spreading ability. Most of the above techniques except degree perform well when the network is complete. The degree centrality does not require complete network information but fails to identify many important nodes due to limited use of only local information. In this paper, we propose a new measure namely “potential edge weight based k-shell degree neighborhood” centrality to rank the node's spreading ability without depending on the degree of completeness of the network. The proposed method uses node degree and k-shell index along with a derived network parameter to assign potential edge weights to the connecting links between two nodes. Information propagation is simulated using Susceptible-Infected-Recovered (SIR) epidemic model and performance comparison of the proposed method is done using Kendall's rank correlation with other ranking techniques. Experiments on real network establish the superiority of the proposed method in identifying influential spreaders in comparison to a degree, k-shell and other standard ranking techniques. Computationally the proposed method is cost-effective even with large complex networks.

Influential spreaders identification in complex networks with improved k-shell hybrid method

Identifying influential spreaders in a complex network has practical and theoretical significance. In applications such as disease spreading, virus infection in computer networks, viral marketing, immunization, rumor containment, among others, the main strategy is to identify the influential nodes in the network. Hence many different centrality measures evolved to identify central nodes in a complex network. The degree centrality is the most simple and easy to compute whereas closeness and betweenness centrality are complex and more time-consuming. The k-shell centrality has the problem of placing too many nodes in a single shell. Over the time many improvements over k-shell have been proposed with pros and cons. The k-shell hybrid (ksh) method has been recently proposed with promising results but with a free parameter that is set empirically which may cause some constraints to the performance of the method. This paper presents an improvement of the ksh method by providing a mathematical model for the free parameter based on standard network parameters. Experiments on real and artificially generated networks show that the proposed method outperforms the ksh method and most of the state-of-the-art node indexing methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value. Due to the absence of any empirically set free parameter, no time-consuming preprocessing is required for optimal parameter value selection prior to actual ranking of nodes in a large network.

Systematic comparison between methods for the detection of influential spreaders in complex networks

Influence maximization is the problem of finding the set of nodes of a network that maximizes the size of the outbreak of a spreading process occurring on the network. Solutions to this problem are important for strategic decisions in marketing and political campaigns. The typical setting consists in the identification of small sets of initial spreaders in very large networks. This setting makes the optimization problem computationally infeasible for standard greedy optimization algorithms that account simultaneously for information about network topology and spreading dynamics, leaving space only to heuristic methods based on the drastic approximation of relying on the geometry of the network alone. The literature on the subject is plenty of purely topological methods for the identification of influential spreaders in networks. However, it is unclear how far these methods are from being optimal. Here, we perform a systematic test of the performance of a multitude of heuristic methods for the identification of influential spreaders. We quantify the performance of the various methods on a corpus of 100 real-world networks; the corpus consists of networks small enough for the application of greedy optimization so that results from this algorithm are used as the baseline needed for the analysis of the performance of the other methods on the same corpus of networks. We find that relatively simple network metrics, such as adaptive degree or closeness centralities, are able to achieve performances very close to the baseline value, thus providing good support for the use of these metrics in large-scale problem settings. Also, we show that a further 2–5% improvement towards the baseline performance is achievable by hybrid algorithms that combine two or more topological metrics together. This final result is validated on a small collection of large graphs where greedy optimization is not applicable.

Identification of influencers in complex networks by local information dimensionality

The identification of influential spreaders in complex networks is a popular topic in studies of network characteristics. Many centrality measures have been proposed to address this problem, but most have limitations. In this paper, a method for identifying influencers in complex networks via the local information dimensionality is proposed. The proposed method considers the local structural properties around the central node; therefore, the scale of locality only increases to half of the maximum value of the shortest distance from the central node. Thus, the proposed method considers the quasilocal information and reduces the computational complexity. The information (number of nodes) in boxes is described via the Shannon entropy, which is more reasonable. A node is more influential when its local information dimensionality is higher. In order to show the effectiveness of the proposed method, five existing centrality measures are used as comparison methods to rank influential nodes in six real-world complex networks. In addition, a susceptible–infected (SI) model and Kendall’s tau coefficient are applied to show the correlation between different methods. Experiment results show the superiority of the proposed method.

Robustness of Coherence in Noisy Scale-Free Networks and Applications to Identification of Influential Spreaders

This brief investigates robustness of the consensus characterized as coherence in the noisy scale-free networks under average degree, random nodal failures, and target attacks. A new centrality index named as leader centrality is proposed to identify more influential spreaders based on the coherence. It is shown that the leaderless and leader–follower consensus is better at the same network size with higher average degree. The obtained results reveal that the consensus becomes worse under the target attacks where the nodes are removed by the descending order of degree, closeness and eigenvector centrality. It is numerically demonstrated that the leader centrality is more effective to identify important spreaders by simulating the spreading process of an SIR epidemic model.

A New K-Shell Decomposition Method for Identifying Influential Spreaders of Epidemics on Community Networks

Abstract An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method, out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.