Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

The analysis and implementation of degree centrality in weighted graph in Social Network Analysis

Abstract: Social Network like Twitter, Facebook, Plurk and Linkedin has million users that constantly evolving and has good information both explicit and implicit information that needs to be explored. Social Network Analysis is the study of social networking with an emphasis on mapping relationships, patterns of interaction between user and content information. One analysis that often be explored is centrality measures that are useful to find the important people in a community by representing in a graph. Kretschmer method is one method of measuring centrality. It is the development of the degree centrality, where the centrality measurement is to find influential people not only involves the number of degree of the user, but also involves the relation weights based on the number of interactions. Therefore, representing the weight graph is very necessary in finding the influential people. Kretschmer method is more suitable for the case of twitter. It is because the most important thing to note is the proximity of the user based on the relationship following/followed and the number of tweet interaction such as mention, retweet and reply. Deeper discussion about the analysis of Kretschmer methods and implementation techniques on twitter is the main discussion of this journal.

Centrality metrics and localization in core-periphery networks

Abstract: Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it has been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to a large scale organization of the network, the core-periphery structure, composed by a dense core plus an outlying and loosely-connected periphery. In this paper we investigate the relation between these two concepts. We consider networks generated via the Stochastic Block Model, or its degree corrected version, with a strong core-periphery structure and we investigate the centrality properties of the core nodes and the ability of several centrality metrics to identify them. We find that the three measures with the best performance are marginals obtained with belief propagation, PageRank, and degree centrality, while non-backtracking and eigenvector centrality (or MINRES}, showed to be equivalent to the latter in the large network limit) perform worse in the investigated networks.

A closeness centrality analysis algorithm for workflow-supported social networks

Abstract: The purpose of this paper is to build a theoretical approach for numerically analyzing closeness centrality measures among workflow-actors of workflow-supported social network models to be formed through BPM(workflow)-driven organizational operations. The essential part of the proposed approach is a closeness centrality analysis equation to calculate each performer's closeness centrality measure on a workflow-supported social network model. In this paper, we try to develop an algorithm that is able to efficiently compute the closeness centrality analysis equation suggested from the conventional social network analysis literature, and eventually the developed algorithm will be applied to analyzing the degree of work-intimacy among those workflow-actors who are allocated to perform the corresponding workflow model.

Searching for Modular Structure in Complex Phenotypes: Inferences from Network Theory

Abstract: The notion of modularity has become a unifying principle to understand structural and functional aspects of biological organization at different levels of complexity. Recently, deciphering the modular organization of molecular systems has been greatly aided by network theory. Nevertheless, network theory is completely absent from the investigation of modularity of complex macroscopic phenotypes, a fundamental level of organization at which organisms experience and interact with the environment. Here, we used geometric descriptors of phenotypic variation to derive a network representation of a complex morphological structure, the mammalian mandible, in terms of nodes and links. Then, by integrating the network representation and description with random matrix theory, we uncovered a modular organization for the mammalian mandible, which deviates significantly from an equivalent random network. The modules revealed by the network analysis correspond to the four morphogenetic units recognized for the mammalian mandible on a developmental basis. Furthermore, these modules are known to be affected only by particular genes and are also functionally differentiated. This study shows that the powerful formalism of network theory can be applied to the discovery of modules in complex phenotypes and opens the possibility of an integrated approach to the study of modularity at all levels of organizational complexity.

A Clustered Approach for Fast Computation of Betweenness Centrality in Social Networks

Abstract: 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 a high impact on computation time if we consider that social networks data are continuously growing and today the number of nodes of the related graphs is in the order of billions. To address the problem, we propose a variant of the Brandes' algorithm for a very fast evaluation of approximated between ness indexes in large networks. In a preliminary phase, our method exploits a scalable and efficient clustering technique, based on Lou vain method, to identify communities and border nodes that guide the selection of a limited number of pivot nodes. The experimental analysis shows that for sparse graphs (which represent a widespread class of social networks graphs) our method drastically reduces the computation time if compared with the most efficient solution for exact evaluation, whereas the degree of approximation is good especially if we are interested to identify the top k between ness values. The scalability analysis conducted on synthetic scale-free graphs also shows that our method behaves well when the network is very large (even though in these cases additional machines could be useful to handle memory consumption).