On Fractional Approach to Analysis of Linked Networks (original) (raw)
Abstracting the core subnet of weighted networks based on link strengths
Journal of the Association for Information Science and Technology, 2014
Most measures of networks are based on the nodes, although links are also elementary units in networks and represent interesting social or physical connections. In this work we suggest an option for exploring networks, called the h-strength, with explicit focus on links and their strengths. The h-strength and its extensions can naturally simplify a complex network to a small and concise subnetwork (h-subnet) but retains the most important links with its core structure. Its applications in 2 typical information networks, the paper cocitation network of a topic (the h-index) and 5 scientific collaboration networks in the field of "water resources," suggest that h-strength and its extensions could be a useful choice for abstracting, simplifying, and visualizing a complex network. Moreover, we observe that the 2 informetric models, the Glänzel-Schubert model and the Hirsch model, roughly hold in the context of the h-strength for the collaboration networks.
Scientometrics, 2013
In the paper we show that the bibliographic data can be transformed into a collection of compatible networks. Using network multiplication different interesting derived networks can be obtained. In defining them an appropriate normalization should be considered. The proposed approach can be applied also to other collections of compatible networks. We also discuss the question when the multiplication of sparse networks preserves sparseness. The proposed approaches are illustrated with analyses of collection of networks on the topic "social network" obtained from the Web of Science.
Hybrid Centrality Measures for Binary and Weighted Networks
Studies in Computational Intelligence, 2013
Existing centrality measures for social network analysis suggest the importance of an actor and give consideration to actor's given structural position in a network. These existing measures suggest specific attribute of an actor (i.e., popularity, accessibility, and brokerage behavior). In this study, we propose new hybrid centrality measures (i.e., Degree-Degree, Degree-Closeness and Degree-Betweenness), by combining existing measures (i.e., degree, closeness and betweenness) with a proposition to better understand the importance of actors in a given network. Generalized set of measures are also proposed for weighted networks. Our analysis of co-authorship networks dataset suggests significant correlation of our proposed new centrality measures (especially weighted networks) than traditional centrality measures with performance of the scholars. Thus, they are useful measures which can be used instead of traditional measures to show prominence of the actors in a network.
Link-space and network analysis
2007
Many networks contain correlations and often conventional analysis is incapable of incorporating this often essential feature. In arXiv:0708.2176, we introduced the link-space formalism for analysing degree-degree correlations in evolving networks. In this extended version, we provide additional mathematical details and supplementary material.
A conservation rule for constructing bibliometric network matrices
ArXiv, 2016
The social network analysis of bibliometric data needs matrices to be recast in a network framework. In this paper we argue that a simple conservation rule requires that this should be done only using fractional counting so that conservation at the paper level will be faithfully reproduced at higher levels ofaggregation (i.e. author, institute, country, journal etc.) of the complex network.
Analytical relationships between metric and centrality measures of a network and its dual
Journal of Computational and Applied Mathematics, 2011
The centrality and efficiency measures of a network G are strongly related to the respective measures on the dual G ⋆ and the bipartite B(G) associated networks. We show some relationships between the Bonacich centralities c(G), c(G ⋆ ) and c(B(G)) and between the efficiencies E(G) and E(G ⋆ ) and we compute the behavior of these parameters in some examples.
Using structure indices for efficient approximation of network properties
2006
Abstract Statistics on networks have become vital to the study of relational data drawn from areas such as bibliometrics, fraud detection, bioinformatics, and the Internet. Calculating many of the most important measures-such as betweenness centrality, closeness centrality, and graph diameter-requires identifying short paths in these networks. However, finding these short paths can be intractable for even moderate-size networks.
A quantitative measure for path structures of complex networks
Europhysics Letters (EPL), 2007
PACS 89.75.Hc-Networks and genealogical trees PACS 89.75.Fb-Structures and organization in complex systems PACS 05.90.+m-Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems Abstract-In this paper we present a generalised version of the classical cluster coefficient, which also can be applied to networks with directed links. This generalisation takes into account more than the immediate nearest neighbours, giving more detailed information about the network structure than the classical version. The introduced concept is compared to earlier generalisation attempts, and it is applied to a directed version of the protein interaction network of the yeast cell S. cerevisiae and networks generated by the growing preferential attachment model of Barabási and Albert. Finally, we give some ideas on how our concept is related to modularity and community structures.
Link Prediction in Highly Fractional Data Sets
Handbook of Computational Approaches to Counterterrorism, Springer
"Extremist organizations all over the world increasingly use online social networks as a communication media for recruitment and planning. As such, online social networks are also a source of information utilized by intelligence and counter terror organizations investigating the relationships between suspected individuals. Unfortunately, the data mined from open sources is usually far from being complete due to the efforts of suspected and known terrorists to hide their relationships. One of the methods used to uncover missing information in social networks is referred to as link prediction. We use link prediction methods solely based on network struc-ture analysis to infer hidden relationships among individuals and investigate their effectiveness in fractional datasets. Experiments performed on a number of closed communities extracted from organizational and public social networks show that structural link prediction retains its effectiveness even when large parts of the origi-nal social network are hidden."
On the relationships between topological measures in real-world networks
Networks and Heterogeneous Media, 2008
Over the past several years, a number of measures have been introduced to characterize the topology of complex networks. We perform a statistical analysis of real data sets, representing the topology of different realworld networks. First, we show that some measures are either fully related to other topological measures or that they are significantly limited in the range of their possible values. Second, we observe that subsets of measures are highly correlated, indicating redundancy among them. Our study thus suggests that the set of commonly used measures is too extensive to concisely characterize the topology of complex networks. It also provides an important basis for classification and unification of a definite set of measures that would serve in future topological studies of complex networks.