Though research on the Semantic Web has progressed at a steady pace, its promise has yet to be realized. One major difficulty is that, by its very nature, the Semantic Web is a large, uncensored system to which anyone may contribute. This raises the question of how much credence to give each source. We cannot expect each user to know the trustworthiness of each source, nor would we want to assign top-down or global credibility values due to the subjective nature of trust. We tackle this problem by employing a web of trust, in which each user maintains trusts in a small number of other users. We then compose these trusts into trust values for all other users. The result of our computation is not an agglomerate "trustworthiness" of each user. Instead, each user receives a personalized set of trusts, which may vary widely from person to person. We define properties for combination functions which merge such trusts, and define a class of functions for which merging may be done locally while maintaining these properties. We give examples of specific functions and apply them to data from Epinions and our BibServ bibliography server. Experiments confirm that the methods are robust to noise, and do not put unreasonable expectations on users. We hope that these methods will help move the Semantic Web closer to fulfilling its promise.

Trust management for the Semantic Web

How to assess the importance of nodes in the network is an open question. There are many ways to identify the importance of nodes in complex networks. However, these methods have their own shortcomings and advantages. In particular, some methods based on the importance of nodes between interactions between nodes have been proposed. These methods utilize local information or path information. How to combine local and global information is still a problem. In this paper, a generalized mechanical model is proposed that uses global information and local information. To verify the effectiveness of the method, some experiments were performed on a total of ten real networks. In particular, an innovative experimental network-based quality assessment was proposed to validate the method of identifying the importance of nodes.

GMM: A generalized mechanics model for identifying the importance of nodes in complex networks

Almost all the complex interactions between humans, animals, biological cells, neurons, or any other objects are now modeled as a graph with the nodes as the objects of interest and interactions as the edges. The identification of the most central or influential node in such a complex network has many practical applications in diverse domains such as viral marketing, infectious disease spreading, rumor spreading in a social network, virus/worm spreading in computer networks, etc. Many centrality measures using the position/location of a node and network structure have been proposed in the literature. The node degree, shortest paths(closeness), and betweenness are used since long with degree capturing local effect and others global effect. The k-shell considers the coreness of the nodes by dividing the network into layers or shells. Many variations of k-shell proposed in recent years, as well as many researchers, use k-shell as a building block in their heuristic technique to alleviate the problems of classical k-shell and to identify influential spreaders more elegantly. The main objective of this paper is to analyze and compare the major variations of the k-shell based methods along with representative network topology based hybrid techniques by considering a toy network with detailed computations. A discussion on different performance evaluation metrics and, simulation models such as the SIR epidemic model, has been undertaken with a comparative analysis between different state-of-the-art on a few standard real networks.

A systematic survey on influential spreaders identification in complex networks with a focus on K-shell based techniques

The influence maximization problem (IMP) is one of the most important topics in social network analysis. It consists of finding the smallest seed of users that maximizes the influence spread in a social network. The main influence spread models are the linear threshold model (LT-model) and the independent cascade model (IC-model). These models have mainly been treated by using the single-objective paradigm which covers just one perspective: maximize the influence spread starting by given seed size, or minimize the seed set to reach a given number of influenced nodes. Sometimes, this minimization problem has been called the least cost influence problem (LCI). In this work, we propose a new optimization model for both perspectives under conflict, through the LT-model, by applying a binary multi-objective approach. Swarm intelligence methods are implemented to solve our proposal on real networks. Results are promising and suggest that the new multi-objective solution proposed can be properly solved in harder instances.

A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods

The users largely contributing towards product adoption or information utilization in social networks are identified by the process of influence maximization. The exponential growth in social networks imposes several challenges in the analyses of these networks. Important has been given to modeling structural properties while the relationship between users and their social behavior has being ignored in the existing literature. With respect to the social behavior, the influence maximization task has been parallelized in this paper. In order to maximize the influence in social networks, an interest based algorithm with parallel social action has been proposed. This is algorithm enables identifying influential users in social network. The interactive behavior of the user is weighted dynamically as social actions along with the interests of the users. These two semantic metrics are used in the proposed algorithm. An optimal influential nodes set is computed by implementing the machines with CPU architecture with perfect parallelism through community structure. This helps in reducing the execution time and overcoming the real-word social network size challenges. When compared to the existing schemes, the proposed algorithm offers improved efficiency in the calculation speed on real world networks.

https://irojournals.com/iroismac/V3/I1/06.pdf

Novel Influence Maximization Algorithm for Social Network Behavior Management

Influence maximization problem aims to select a subset of k most influential nodes from a given network such that the spread of influence triggered by the seed set will be maximum. Greedy based algorithms are time-consuming to approximate the expected influence spread of given node set accurately and not well scalable to large-scale networks especially when the propagation probability is large. Conventional heuristics based on network topology or confined diffusion paths tend to suffer from the problem of low solution accuracy or huge memory cost. In this paper an effective discrete shuffled frog-leaping algorithm (DSFLA) is proposed to solve influence maximization problem in a more efficient way. Novel encoding mechanism and discrete evolutionary rules are conceived based on network topology structure for virtual frog population. To facilitate the global exploratory solution, a novel local exploitation mechanism combining deterministic and random walk strategies is put forward to improve the suboptimal meme of each memeplex in the frog population. The experimental results of influence spread in six real-world networks and statistical tests show that DSFLA performs effectively in selecting targeted influential seed nodes for influence maximization and is superior than several state-of-the-art alternatives.

Li Fan

Papers

A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks