10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      A Survey of Heterogeneous Information Network Analysis

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references 167

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The structure and function of complex networks

           M. Newman (2003)
          Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Normalized cuts and image segmentation

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Finding and evaluating community structure in networks

               M. Newman,  M Girvan (2003)
              We propose and study a set of algorithms for discovering community structure in networks -- natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.
                Bookmark

                Author and article information

                Journal
                IEEE Transactions on Knowledge and Data Engineering
                IEEE Trans. Knowl. Data Eng.
                Institute of Electrical and Electronics Engineers (IEEE)
                1041-4347
                January 1 2017
                January 1 2017
                : 29
                : 1
                : 17-37
                Article
                10.1109/TKDE.2016.2598561
                © 2017

                Comments

                Comment on this article