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

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.

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

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

Identification of influential users in social networks based on users interest

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

Influence maximization aims to select a small set of k influential nodes to maximize the spread of influence. It is still an open research topic to develop effective and efficient algorithms for the optimization problem. Greedy-based algorithms utilize the property of “submodularity” to provide performance guarantee, but the computational cost is unbearable especially in large-scale networks. Meanwhile, conventional topology-based centrality methods always fail to provide satisfying identification of influential nodes. To identify the k influential nodes effectively, we propose a metaheuristic discrete bat algorithm (DBA) based on the collective intelligence of bat population in this paper. According to the evolutionary rules of the original bat algorithm (BA), a probabilistic greedy-based local search strategy based on network topology is presented and a CandidatesPool is generated according to the contribution of each node to the network topology to enhance the exploitation operation of DBA. The experimental results and statistic tests on five real-world social networks and a synthetic network under independent cascade model demonstrate that DBA outperforms other two metaheuristics and the Stop-and-Stair algorithm, and achieves competitive influence spread to CELF (Cost-Effective Lazy Forward) but has less time computation than CELF.

Maximizing the spread of influence via the collective intelligence of discrete bat algorithm

The basic task of the logistics distribution center is to achieve the storage and distribution of materials, and to plan, implement, and manage the effective flow of materials from the supplying place to the consumption place. Scientific location of logistics distribution center can effectively reduce logistics cost, improve the speed of circulation, increase the profits of enterprises, and enhance the core competitiveness of enterprises. Combining the advantages of K-means clustering algorithm, this paper applies it to the location problem of logistics distribution center and proposes a logistics distribution center location method combining K-means clustering theory and D-S reasoning, which provides a better solution for the location problem of logistics distribution center. Through case analysis, K-means clustering algorithm can obtain reasonable location of logistics distribution center, which can be applied to the location of multilevel logistics distribution network, and has certain practical application value.

Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm

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

Maximizing the spread of influence via the collective intelligence of discrete bat algorithm

Research on Location of Logistics Distribution Center Based on K-Means Clustering Algorithm