Social networks play a significant role in spreading data in individuals&#x0027; day-to-day existence. In viral advertising, organizations desire to communicate their items by utilizing the network organization and qualities of influence propagation. In particular, they need to give items at no cost to the chosen clients (seed nodes), permit them to promote them throughout the network and maximize the acquisition. There should be a spreading plan of the free-of-charge items with the objective of the organizations to choose the ideal seed set to boost the influence spread. This issue is known as influence maximization (IM) and has a broad scope viz., suggestion frameworks, link prediction, and data diffusion. In this paper, we worked on finding a connection between the model execution and the size of the chart and finding the seed hub. Impact Maximization (IM) is a principal issue to recognize a tiny arrangement of people which contain maximal impact increase within the community system. 

A Learning Based Influence Maximization across Multiple Social Networks

It has theoretical interest and practical significance to find out influential nodes which make the information spread faster and more extensive in complex networks. A variety of centrality measure...

A novel centrality measure for identifying influential nodes based on minimum weighted degree decomposition

Now-a-days, social networks have become an important part of the individuals' day-to-day life. It plays a significant role in spreading data over the network. In viral advertising, organizations desire to propagate their items by utilizing the network of the organization and potentials of the influence propagation. Usually, they give the items at no cost to the chosen clients, promote them throughout the network, and maximize the acquisition. There should be a good spreading plan for the free-of-charge items with the objective of the organizations for choosing the ideal seed set to boost the influence spread. Identifying the seed nodes in the network which can maximize the influence is known as Influence Maximization (IM). Influence maximization is a principal issue to recognize a tiny arrangement of people which contain maximal impact within the community. Centrality measures help to identify the influential node in the network. This paper presents a comparative analysis of the effectiveness of the Influence Maximization techniques viz., Heuristic Algorithm (HA), Greedy Algorithm (GA), Cost Effective Lazy Forward (CELF) ++ Algorithm, and Discrete Shuffled Frog-Leaping Algorithm (DSFLA). We evaluated the performance on various social network datasets available in the public domain viz., Facebook, Skitter, Epinions, and Twitter. We observed that DSFLA is an efficient technique that outperforms in terms of the computational time as compared to the other influence maximization techniques viz., HA, GA, and CELF++.

A Comparative Analysis of the Techniques for Influence Maximization in Social Networks

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks, solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex’s importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Identifying multiple influential spreaders is a significant procedure to understand, control or accelerate the dynamics of information diffusion process in complex networks effectively. For a given network [Formula: see text] and an integer [Formula: see text], we need to find a set of [Formula: see text] vertices as “seeds” which carry the information originally, and then through a certain diffusion model, the information can be spread as widely as possible. Note that these seeds cannot be too close to each other, otherwise information is easy to be congested. In this paper, we make an attempt to identify multiple spreaders by considering the “marginal benefit” [Formula: see text] of a vertex [Formula: see text] when it is added to an existing seed set [Formula: see text]. Here [Formula: see text] is defined as a function of [Formula: see text]’s influential ability and the common influence range between vertex [Formula: see text] and [Formula: see text]. In particular, we use the degree to measure the vertex’s influential ability and use the number of common neighbors between vertex [Formula: see text] and [Formula: see text] to measure their common influence range. In order to verify this new algorithm’s validity, we apply it on several social networks and the new method performs better than others. This new method is simple to implement and has lower time complexity, thus is expected to have promising applications in the future. 

CSS: A new Combined Spreading Score Measurement for Identifying Multiple Influential Spreaders in Complex Networks

Identifying influential spreaders is of great significance to the information diffusion, the identifying of hub protein, the control of infectious diseases For multiple spreaders, an ideal situation is that not only the spreaders themselves are influential but also relatively dispersed to effectively reduce overlaps However, it is difficult to make a good tradeoff between them In this paper, the maximum connected component decomposition method (MCCD) is proposed to identify influential spreaders in complex networks In this method, different topological attributes of nodes are comprehensively considered and combined with the decomposition method of maximum connected components (MCC) with the topological features Firstly, the nodes are reranked according to the comprehensive consideration of network topology information Then, the nodes with higher rankings in the network are checked If the size and number of the largest connected components in the network are the smallest after deleting a node, the node is selected as the new spreader When multiple nodes have the same size and number of the maximum connected components, values of which are minimal in all cases, topology information for other connected components of these nodes is considered Moreover, the method can identify initial spreaders that are not the highest ranking but have great impacts on the network, including the spreading speed, propagation range, and distribution range of initial spreaders Experimental studies in the Susceptible–Infected–Recovered (SIR) model are shown in four networks to verify the performance of our proposed method along with seven centrality-based and heuristic methods

Identifying multiple influential spreaders based on maximum connected component decomposition method

The invention relates to a complex network key node cluster mining method, and specifically discloses a complex network key node cluster mining method based on combination optimization, which is usedfor solving problems of simplified technical indexes, high cost needed for node mining, limited propagation range, insufficient amount of propagation information and no consideration of a strengthening function of reachable neighbors to node centrality in the existing method. The method provided by the invention has the following steps: 1, building an input network model; 2, selecting indexes forpreprocessing network nodes; 3, calculating an initial key node cluster S and a to-be-optimized node cluster S ; 4, selecting an objective function of key node cluster combination optimization; 5, performing combination optimization; 6, outputting a key node cluster S. The method provided by the invention has the advantages of preprocessing network node groups, selecting limited nodes for performing combination optimization, being low-complexity in calculation, considering various indexes, preprocessing the network nodes and performing combination optimization for the obtained nodes, andmining the key node cluster which needs low cost for mining, has wide propagation range and large amount of propagation information, and considers the strengthening function of the reachable neighborsto node attributes.

Complex network key node cluster mining method based on combination optimization

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Identifying multiple influential spreaders based on maximum connected component decomposition method

Complex network key node cluster mining method based on combination optimization