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In recent past, Online Social Networks (OSN) has emerged as a platform for sharing information, thoughts, and activities. In the real-world network, method of considering the appropriate samples is most frequently used for network analysis. Graph sampling is a procedure used for computing unknown parameters. Many sampling algorithms exist in literature such as Random node, Random edge sampling, Rank degree, etc. can be used for estimation. This paper presents a comparison of clique based procedure (CBP) and shortest path based procedure (SPP) to estimate the average degree of a vertex in a social network using an overlapping cluster sampling. A comparative procedure is used to obtain the lower and upper limit of confidence intervals with the help of multiple samples. Ogive based simulation is also used for single value computation of limits of CI. The results, obtained from simulation, show that clique based sampling algorithm (CBP) is more efficient than the shortest path based sampling algorithm (SPP). The estimated confidence intervals can be used for monitoring the reliability of a social network in terms of control over average network degree. [ABSTRACT FROM AUTHOR]

This research proposes the exemplification of a mapping of a social network. Thus, in this research, a social network of entities related to the Federal University of Tocantins (UFT) was reproduced using 13 thousand digital documents. To map the connections of the documents and generate the graph, it was used as a criterion that if the name of two persons are in the same document they have a connection, for each different file that has a connection, this relationship is strengthened. The graph had 114,405 vertices, and 21,081,984 edges. Of the ten most central nodes, the entities found occupied, or still occupy, the following functions: dean, campus director, pro-rector and vice-rector. In this way, the reproduction of a complex network of relationships is considered, with the use of digital documents as a viable alternative for the mapping of social interactions of nuclei that are probably not mapped by conventional online social networks. Thus, the graph created by this social network model, built from digital text documents, presents an alternative to map relationships between entities, which can have different purposes. [ABSTRACT FROM AUTHOR]

The inherent structure and connectivity of a group are important features of social networks. Finding the densest subgraphs of a graph directly maps to revealing the densest communities of a social network. Various techniques, e.g., edge density, k -core, near-cliques and k -cliques, have been developed to characterize graphs and extract the densest subgraphs of the graphs. However, as extraction of subgraphs with constraints is NP-hard, these techniques face a major difficulty of processing big and/or streaming data sets from social networks. This demands new methods from the expert and intelligent systems perspective for computation of the densest subgraph problem (DSP) with big and/or streaming data. The most recent method for this purpose is the “Sampling” method. It samples the big data sets, thus reducing the data space and consequently speeding up the DSP computation. But the sampled data inevitably miss out many useful data items. A new approach is presented in this paper for accelerated DSP computation with big and/or steaming data through data space reduction without loss of useful information. It uses a sliding window of small graphs with a fixed number of edges. Then, it filters out the least connected edges for each small graph. While the small graphs are processed, subgraphs are incrementally put together to reveal the densest subgraphs. Finally, the data space previously filtered out is checked for recovery of globally important edges. The approach is incorporated with existing subgraph extraction techniques for scalable and efficient DSP computation with improved accuracy. It is demonstrated for four subgraph extraction techniques over four Twitter data sets, and is shown to outperform the “sampling” method. [ABSTRACT FROM AUTHOR]

Many approaches have been proposed to perform the analysis of network(graph) data correlated with Internet, social networks, communication networks, knowledge graph etc, but few of them have been applied in the coordinate system which is thought to be an eficient platform to processing graph data. In this survey, we provide a short yet structured analysis of network(graph) data research in the coordinate system. We First introduce the coordinate embedding and transforming of double-loop network with its symmetrical and regular structure. We then present two categories of approaches for Internet embedding in the coordinate system and the technology of graph embedding of social network. Finally, we draw our conclusions and discuss potential applications and future research direction. [ABSTRACT FROM AUTHOR]

Social bookmarking systems have established themselves as an important part in today’s Web. In such systems, tag recommender systems support users during the posting of a resource by suggesting suitable tags. Tag recommender algorithms have often been evaluated in offline benchmarking experiments. Yet, the particular setup of such experiments has rarely been analyzed. In particular, since the recommendation quality usually suffers from difficulties such as the sparsity of the data or the cold-start problem for new resources or users, datasets have often been pruned to so-called cores (specific subsets of the original datasets), without much consideration of the implications on the benchmarking results. In this article, we generalize the notion of a core by introducing the new notion of a set-core, which is independent of any graph structure, to overcome a structural drawback in the previous constructions of cores on tagging data. We show that problems caused by some types of cores can be eliminated using set-cores. Further, we present a thorough analysis of tag recommender benchmarking setups using cores. To that end, we conduct a large-scale experiment on four real-world datasets, in which we analyze the influence of different cores on the evaluation of recommendation algorithms. We can show that the results of the comparison of different recommendation approaches depends on the selection of core type and level. For the benchmarking of tag recommender algorithms, our results suggest that the evaluation must be set up more carefully and should not be based on one arbitrarily chosen core type and level. [ABSTRACT FROM AUTHOR]