Spreaders In Social Networks Research Articles

Identifying influential spreaders in social networks: A two-stage quantum-behaved particle swarm optimization with Lévy flight

The influence maximization problem aims to select a small set of influential nodes, termed a seed set, to maximize their influence coverage in social networks. Although the methods that are based on a greedy strategy can obtain good accuracy, they come at the cost of enormous computational time, and are therefore not applicable to practical scenarios in large-scale networks. In addition, the centrality heuristic algorithms that are based on network topology can be completed in relatively less time. However, they tend to fail to achieve satisfactory results because of drawbacks such as overlapped influence spread. In this work, we propose a discrete two-stage metaheuristic optimization combining quantum-behaved particle swarm optimization with Lévy flight to identify a set of the most influential spreaders. According to the framework, first, the particles in the population are tasked to conduct an exploration in the global solution space to eventually converge to an acceptable solution through the crossover and replacement operations. Second, the Lévy flight mechanism is used to perform a wandering walk on the optimal candidate solution in the population to exploit the potentially unidentified influential nodes in the network. Experiments on six real-world social networks show that the proposed algorithm achieves more satisfactory results when compared to other well-known algorithms.

Improved VoteRank Algorithm to Identify Crucial Spreaders in Social Networks

Improved VoteRank Algorithm to Identify Crucial Spreaders in Social Networks

Characterizing and predicting fake news spreaders in social networks

Characterizing and predicting fake news spreaders in social networks

Identifying Influential Spreaders in Social Networks Through Discrete Moth-Flame Optimization

Influence maximization in a social network refers to the selection of node sets that support the fastest and broadest propagation of information under a chosen transmission model. The efficient identification of such influence-maximizing groups is an active area of research with diverse practical relevance. Greedy-based methods can provide solutions of reliable accuracy, but the computational cost of the required Monte Carlo simulations renders them infeasible for large networks. Meanwhile, although network structure-based centrality methods can be efficient, they typically achieve poor recognition accuracy. Here, we establish an effective influence assessment model based both on the total valuation and variance in valuation of neighbor nodes, motivated by the possibility of unreliable communication channels. We then develop a discrete moth-flame optimization method to search for influence-maximizing node sets, using a local crossover and mutation evolution scheme atop the canonical moth position updates. To accelerate convergence, a search area selection scheme derived from a degree-based heuristic is used. The experimental results on five real-world social networks, comparing our proposed method against several alternatives in the current literature, indicates our approach to be effective and robust in tackling the influence maximization problem.

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.

Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the success of online social networks without ensuring the existence of trust between nodes. Detecting influential nodes improves collaborative filtering (CF) recommendations in which nodes with the highest influential capability are most likely to be the source of recommendations. Although CF-based recommendation systems are the most widely used approach for implementing recommender systems, this approach ignores the mutual trust between users. In this paper, a trust-based algorithm (TBA) is introduced to detect influential spreaders in social networks efficiently. In particular, the proposed TBA estimates the influence that each node has on the other connected nodes as well as on the whole network. Next, a Friend-of-Friend recommendation (FoF-SocialI) algorithm is addressed to detect the influence of social ties in the recommendation process. Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented. The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks.

A new local and multidimensional ranking measure to detect spreaders in social networks

Spreaders detection is a vital issue in complex networks because spreaders can spread information to a massive number of nodes in the network. There are many centrality measures to rank nodes based on their ability to spread information. Some local and global centrality measures including DIL, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, PageRank centrality and k-shell decomposition method are used to identify spreader nodes. However, they may have some problems such as finding inappropriate spreaders, unreliable spreader detection, higher time complexity or incompatibility with some networks. In this paper, a new local ranking measure is proposed to identify the influence of a node. The proposed method measures the spreading ability of nodes based on their important location parameters such as node degree, the degree of its neighbors, common links between a node and its neighbors and inverse cluster coefficient. The main advantage of the proposed method is to clear important hubs and low-degree bridges in an efficient manner. To test the efficiency of the proposed method, experiments are conducted on eight real and four synthetic networks. Comparisons based on Susceptible Infected Recovered and Susceptible Infected models reveal that the proposed method outperforms the compared well-known centralities.

Ranking nodes in complex networks based on local structure and improving closeness centrality

In complex networks, the nodes with most spreading ability are called influential nodes. In many applications such as viral marketing, identification of most influential nodes and ranking them based on their spreading ability is of vital importance. Closeness centrality is one of the most commonly used methods to identify influential spreaders in social networks. However, this method is time-consuming for dynamic large-scale networks and has high computational complexity. In this paper, we propose a novel ranking algorithm which improves closeness centrality by taking advantage of local structure of nodes and aims to decrease the computational complexity. In our proposed method, at first, a community detection algorithm is applied to extract community structures of the network. Thereafter, after ignoring the relationship between communities, one best node as local critical node for each community is extracted according to any centrality measure. Then, with the consideration of interconnection links between communities, another best node as gateway node is found. Finally, the nodes are sorted and ranked based on computing the sum of the shortest path length of nodes to obtained critical nodes. Our method can detect the most spreader nodes with high diffusion ability and low time complexity, which make it appropriately applicable to large-scale networks. Experiments on synthetic and real-world connected networks under common diffusion models demonstrate the effectiveness of our proposed method in comparison with other methods.

Identifying influential spreaders in complex networks based on gravity formula

How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible–Infected–Recovered (SIR) epidemic model to verify the good performance of our method.

Identifying Topic-sensitive Influential Spreaders in Social Networks

Identifying influential spreaders is an important issue in understanding the dynamics of information diffusion in social networks. It is to find a small subset of nodes, which can spread the information or influence to the largest number of nodes. The conventional approaches consider information diffusion through the network in a coarse-grained manner, without taking into account the topical features of information content and users. However, for messages with different topics, the target influential spreaders may vary largely. In this paper, we propose to harness historical propagation data to learn the information diffusion probabilities on topic-level, based on which we use a greedy algorithm to iteratively select a set of influential nodes for a given topic. Specially, we design a three-stage algorithm named TopicRank to mine the most influential spreaders with respect to a specific topic. Given observed propagation data, we first use Latent Dirichlet Allocation (LDA) model to learn a topic mixture for each propagation message. Then, the topic-level diffusion probability of an edge is computed by exploiting the propagation actions occurred to it and the topic distribution of these propagation messages. Last, based on the learned topic-level diffusion probabilities, we apply optimized greedy algorithm CLEF to identify influential nodes with respect to a specific topic. Experimental results show that our method significantly outperforms state-of-the-art methods when used for topic-sensitive information spread maximization.

Leveraging neighborhood “structural holes” to identifying key spreaders in social networks

The identifying of influential nodes in large-scale complex networks is an important issue in optimizing network structure and enhancing robustness of a system. To measure the role of nodes, classic methods can help identify influential nodes, but they have some limitations to social networks. Local metric is simple but it can only take into account the neighbor size, and the topological connections among the neighbors are neglected, so it can not reflect the interaction between the nodes. The global metrics is difficult to use in large social networks because of the high computational complexity. Meanwhile, in the classic methods, the unique community characteristics of the social networks are not considered. To make a trade off between affections and efficiency, a local structural centrality measure is proposed which is based on nodes' a nd their ‘neighbors’ structural holes. Both the node degree and “bridge” property are reflected in computing node constraint index. SIR (Susceptible-Infected-Recovered) model is used to evaluate the ability to spread nodes. Simulations of four real networks show that our method can rank the capability of spreading nodes more accurately than other metrics. This algorithm has strong robustness when the network is subjected to sybil attacks.