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Friend recommendation service is a common and important demand for the users on various online platforms. Current studies mainly focus on making predictions with the neighborhood and path information derived from the personal relationship networks. However, the formed links do not indicate that two users are familiar with each other nor have intimate connections. Selective treatments are made according...
The Service Oriented Computing (SOC) paradigm promotes building new applications by discovering and then invoking services, i.e., software components accessible through the Internet. Discovering services means inspecting registries where textual descriptions of services functional capabilities are stored. To automate this, existing approaches index descriptions and associate users' queries to relevant...
The problem of node classification has been widely studied in a variety of network-based scenarios. In this paper, we will study the more challenging scenario in which some of the edges in a content-based network are labeled, and it is desirable to use this information in order to determine the labels of other arbitrary edges. Furthermore, each edge is associated with text content, which may correspond...
Finding the key nodes of the network is one of the most important applications in Community Detection. This paper combined Centrality, Cluster Coefficient and PageRank ideas, establish the measurement system of key nodes and according to the Kendal correlation coefficient of each index to modify the measurement system. Finally, using the Fagin algorithm to carry on the key nodes of the multidimensional...
Now a day Social media communication become to important factor for business operation. Several Customer prefers to post their comment, suggestion, complaints about company's products and services to online media such as Facebook, Twitter, Social web board because it easy way to blast to public and increases pressure to product owner for responding. This is one factor that cooperate need to be concern...
Link prediction in network attempts to predict the exist-yet-unknown links or future links in accordance with the node properties and the network typology. It has been used in many domains such as social network, biology experiment, and criminal investigations. Classical methods are based on graph topology structure and path features but few consider clustering information. Actually, clustering information...
Link prediction is a key task to identify the future links among existing non-connected members of a network, by measuring the proximity between nodes in a network. Node neighbourhood based link prediction techniques are immensely used for prediction of future links. These techniques can be applied on various applications like biological protein- protein interaction network, social network, information...
In the last few years, the data generated by social networking systems have become interesting to analyze local and global social phenomena. A useful metric to identify influential people or opinion leaders is the between ness centrality index. The computation of this index is a very demanding task since its exact calculation exhibits O(nm) time complexity for unweighted graphs. This complexity has...
Organizations measure their social audience based on the number of users, fans, and followers on social media. Every social media platform has its user identity and a single user is present across varied platforms. Due to the disconnected user profiles, identifying duplicate users across media is non-trivial. There is a need to create a complete view of a user for various applications such as targeting...
Crime analysis has been widely studied, but problem of identifying conspirators through communication network analysis is still not well resolved. In this paper, we proposed a fuzzy clustering algorithm to detect hidden criminals from topic network, which took no use of individuals' prior identity information. We first built up a local suspicion calculation from nodes' neighboring information (node...
This paper extends our previous effort in employing transitivity attributes of graphs for social network analysis. Specifically, here we focus on the problem of network community detection. We propose spectral analysis of the transitivity gradient matrix and compare our framework to the modularity based community detection that attracted many network researchers' attention recently. Previously, we...
In this study we analyze behavior of two types of coefficients for determining the suitable number of clusters obtained when fuzzy cluster analysis is applied. First one is Dunn's coefficient which contains membership degrees in its computational formula; second one is the average silhouette width, used primarily for evaluating hard clustering. There have already been attempts to compare different...
Social networking sites, such as YouTube, Flickr, Livejournal and Orkut, are getting popular with the increasing number of Internet users. As a consequence, community detection on social network sites becomes more important and have been studied by many researchers. However, most of the proposed algorithms are used to detect communities on small scale social networks which limits studying large scale...
A lot of algorithms in communities detection have been proposed particularly for undirected networks. As methods to find communities in directed networks are few, our contribution is to propose a method based on strongly and unilaterally connected components, and more specifically on strongly p-connected components in directed graphs. The result is a clustering of nodes giving good results in generated...
Some methods for object group identification applicable for social group identification are compared. We suppose that people are characterized by their actions, for example the deputies are characterized by their voting habits. We are interested in binary data analysis (e.g. the result of voting is yes or not). The dataset consisting of the roll-call votes records in the Russian parliament in 2004...
Cohesion is one of key factors influencing the performance of collaborative learning. But it is hart to reckon and assess it in online collaborative learning environments. Social network analysis (SNA) is a new method and technique analyzing the properties of the interaction relations between participants. In this paper, we introduced the related key concepts in SNA, and presented an analysis model...
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